The U.S. Court of Appeals for the Seventh Circuit recently concluded, in an appeal of a denial of a motion to certify a class, that suing on a time-barred consumer debt violates the federal Fair Debt Collection Practices Act (FDCPA), and that a debt collector may violate the FDCPA by filing a collection lawsuit even if the consumer is never actually served with the lawsuit.
The Court also held that the district court should have ruled on the dispute between the parties as to the correct statute of limitations period prior to denying the plaintiff's motion to certify a class, as it was both a merits and procedural issue because it determined the composition of the class. The Court also included troubling dicta about attacks on the adequacy of class representatives.
In so ruling, the Seventh Circuit reversed the district court's denial of plaintiff's motion for class certification, and remanded the matter to the district court for further proceedings.
A copy of the Court's opinion is attached.
The case involves a consumer who was sued by a debt collector for a debt arising from the consumer's purchase of natural gas for household use. The consumer responded by bringing a purported class action against the debt collector, alleging the debt collector had initiated its collection action after the statute of limitations in violation of the federal Fair Debt Collection Practices Act. The plaintiff's complaint also contained supplemental claims under Illinois and other states' laws, making similar allegations. The plaintiff sought to have the purported class certified.
According to the data compiled for use in addressing the issue of certification, the class the plaintiff wanted certified had 793 members, of whom 343 resided in Illinois. There was virtually no information about the other potential members. Of the 343 Illinois residents, 290 were sued between four and five years after the claims against them had accrued and 45, including the plaintiff, were sued more than five years after accrual. No information was known about the remaining 8 Illinois putative class members. Of the 45 claimants sued by the debt collector after more than five years, 23 were served and 22 were not.
One of the primary issues was whether Illinois' four-year statute of limitations for suits on sale contracts, 810 ILCS 5/2-725(1), or the five-year statute of limitations for suits on unwritten contracts under 735 ILCS 5/13-205 applied. Although the parties did not raise the issue, the statute of limitations on written contracts is ten years, 735 ILCS 5/13-206, and the natural gas contracts on which the debts were based were written. Both the four and ten year statute of limitations have an exception to contracts governed by section 2-725 of the UCC, which is four years. The district court, however, never addressed the statute of limitations issue in denying class certification.
Instead, district court focused on the plaintiff's status and the lack of a sufficient number of claimants to justify a class action. Because the plaintiff had been sued after five years, the district court concluded that she was not an adequate representative of the entire class because, according to the district court, the plaintiff had little to no incentive to argue for a four year statute of limitations on behalf of the other claimants. The district court then isolated the 45 claimants who, like plaintiff, had been sued after five years.
The district court held that because 22 of those claimants were never served, the class was only composed of 23 claimants, a number too small to justify class certification.
On appeal, the Seventh Circuit found several flaws with the district court's analysis of whether class certification was appropriate for purposes of pursuing claims against the debt collector based on the debt collector's untimely suits against consumers. The Seventh Circuit, citing to a 1987 opinion from a federal court in Alabama, recognized the dangers associated with allowing stale claims to collect debts from consumers.
The Seventh Circuit held that suing on time-barred consumer debts violates the FDCPA, citing to Huertas v. Galaxy Asset Mgmt., 641 F.3d 28, 32–33 (3d Cir. 2011), Harvey v. Great Seneca Financial Corp., 453 F.3d 324, 332–33 (6th Cir. 2006), and Herkert v. MRC Receivables Corp., 655 F. Supp. 2d 870, 875–76 (N.D. Ill. 2009).
According to the Seventh Circuit, as with any defendant sued on a stale claim, the passage of time not only dulls the consumers' memory of the circumstances and validity of the debt, but heightens the probability that she will no longer have personal records detailing the status of the debt. Indeed, according to the Seventh Circuit, the unfairness of such conduct is particularly clear in the consumer context where courts have imposed a heightened standard of care – that sufficient to protect the least sophisticated consumer. The Seventh Circuit further noted that, because few unsophisticated consumers would be aware that a statute of limitations could be used to defend against lawsuits based on stale debts, such consumers would unwittingly acquiesce to such lawsuits. And, the Court continued, even if the consumer realizes that she can use time as a defense, she will more than likely still give in rather than fight the lawsuit because she must still expend energy and resources and subject herself to the embarrassment of going into court to present the defense, and this -- the Court noted -- is particularly true in light of the costs of attorneys today.
Next, the Seventh Circuit concluded that the district court erred by determining that the plaintiff was not an adequate representative of the claimants based on her status of being sued after five years.
The Seventh Circuit stated: "To question her adequacy is to be unrealistic about the role of the class represented in a class action suit. The role is nominal. The class representative receives modest compensation for what usually are minimal services in the class action suit. For class action attorneys are the real principals and the class representatives/clients their agents."
The Seventh Circuit went on to state that the district court judge, if not willing to appoint a second class representative for those claimants sued between four and five years after the debt accrued, should have ruled on whether the statute of limitations was four or five years. The debt collector argued that it would have been wrong for the district court to address the statute of limitations issue because it was a merits issue and not one of class action procedure, but the Seventh Circuit disagreed and determined that the statute of limitations was both a merits and procedural issue because it determined the composition of the class.
Having determined that the four year statute of limitation exception under the UCC applied to the purchase of natural gas for household use, the Seventh Circuit finally addressed the district court's exclusion from the class those consumers who the debt collector sued but never served the complaint.
The Seventh Circuit concluded that the district court should not have excluded those claimants. The Seventh Circuit stated: "The debt collector may also use the pending legal action to pressure a debtor to pay back the debt informally, without serving the complaint – precisely the type of unfair practice prohibited by the FDCPA. But even if no debtors were ever harmed by being sued but not served, the district judge would have been wrong to exclude from the class the debtors who were not served. Proof of injury is not required when the only damages sought are statutory."
Accordingly, the Seventh Circuit reversed the district court's denial of class certification and remanded the matter to the district court to determine the proper scope of the class. The Seventh Circuit instructed the district court that the scope need not be limited to Illinois residents or to claims under the FDCPA, and that the district court, if necessary, could create subclasses to address different state law allegations.
The U.S. Court of Appeals for the Seventh Circuit recently held that, under the Illinois mortgage recording statute in effect prior to a June 2013 corrective amendment occasioned by this litigation, the absence of a maturity date and/or an interest rate in a mortgage instrument did not allow a bankruptcy trustee to avoid a mortgage through the trustee's "strong-arm" powers under 11 U.S.C. § 544(a)(3). A copy of the Court's opinion is attached.
The Seventh Circuit addressed appeals from two separate bankruptcy cases. In both matters, the recorded mortgages did not state the maturity date of the secured debt or the applicable interest rate. Each mortgage did however incorporate by reference their respective promissory notes, which included these terms.
The bankruptcy trustee in each matter filed adversary actions under 11 U.S.C. § 544(a)(3) seeking to avoid the mortgages because they did not state the maturity dates or interest rates for the secured dates. In one of the matters, the bankruptcy court granted summary judgment in favor of the trustee, but this ruling was reversed by the district court judge. In the other action, the bankruptcy court granted summary judgment in favor of the mortgage lender. The bankruptcy trustees appealed in both actions.
As you may recall, 11 U.S.C. § 544(a)(3) provides a trustee with so-called "strong-arm" powers which provide the trustee the "rights and powers of … a bona fide purchaser of real property" to void any obligation of the debtor or avoid the transfer of property. Importantly, the strong arm power makes any actual notice of the trustee irrelevant.
As the Seventh Circuit noted, the determination of whether a party is a bona fide purchaser and what constitutes constructive notice is governed by state law. Accordingly, in rejecting the trustees' appeals, the Court's analysis was based upon Illinois law.
In Illinois, a bona fide purchaser is one who acquires an "interest in [the] property for valuable consideration without actual or constructive notice of another's adverse interest in the property." U.S. Bank N.A. v. Villasenor, 979 N.E.2d 451 (Ill. App. 2012). Under the strong-arm statute the trustee cannot be charged with actual notice; however, constructive notice is effective against a trustee.
Constructive notice, in Illinois, is defined as knowledge that the law imputes to a purchaser, whether or not the purchaser had actual knowledge at the time of the conveyance. U.S. Bank., 979 N.E.2d at 465. There are two kinds of constructive notice: record notice and inquiry notice. Record notice imputes to a purchaser knowledge that could be gained from an examination of the grantor-grantee index in the office of the Recorder of Deeds, as well as the probate, circuit, and county court records for the county in which the land is situated.
The trustees argued that each respective mortgage was legally insufficient to give constructive notice to a bona fide purchaser, because the instruments did not provide all the required information under the Illinois mortgage recording statute, 765 ILCS 5/11 (2012).
As may recall, 765 ILCS 5/11 (2012), in relevant part provides a form for which mortgages recorded in Illinois to follow. However, the statute also provides that "Mortgages of lands may be substantially in the following form." Id.
The Court noted that the mortgages at issue substantially followed the suggested form, and specifically provided that the underlying debts were secured by separate but contemporaneously signed promissory note. However, the mortgages did not set forth the maturity dates or the interest rates of the underlying loans, information which was provided for in the Illinois statutory form.
Applying the Illinois rules of statutory interpretation and attempting to predict how the Illinois Supreme Court would resolve the issue, the Seventh Circuit determined that the form in the Illinois mortgage recording statute was permissive and not mandatory. Thus, the trustee's section strong-arm powers could not avoid the mortgages at issue.
In so holding, the Seventh Circuit noted that statutes operative language was clearly permissive. Accordingly, the Court determined that strict compliance with the suggested form is not required to ensure a valid mortgage enforceable against subsequent lenders and purchasers.
The Seventh Circuit then examined the Illinois common law to determine which elements were required in a mortgage for it to be effective as to subsequent bona fide purchasers. In doing so, the Court first noted that the trustees failed to cite and indeed the Court did not find any Illinois cases holding that a recorded mortgage must state the maturity date and/or the interest rate to ensure their validity, enforceability, and priority. According to the Illinois Supreme Court, as noted by the Seventh Circuit, the "essential things" in a mortgage "are the existence of a debt and the intention to secure its payment." Caraway v. Sly, 78 N.E. 588, 589 (Ill. 1906).
The Court further noted that under Illinois law the amount of the debt is an indispensable element of a mortgage, and must be included in a recording for it to be effective against a third party. However, the Seventh Circuit was not persuaded by the trustees' arguments that the interest rate and maturity date were similarly indispensable elements, and determined that providing these terms was not necessary to give constructive record notice of the mortgage to subsequent lenders and purchasers.
Accordingly, the Seventh Circuit held that the trustees had constructive notice of the mortgages in both of the cases on appeal, and the trustees could not rely upon their strong-arm powers to avoid the mortgages.
Notably, the amended version of 765 ILCS 5/11 which went into effect on June 1, 2013, explicitly provides that the provisions in the suggested form "are, and have always been, permissive and not mandatory" and the specifically the "failure to state the interest rate or the maturity date, or both, shall not affect the validity or priority of the mortgage." See 765 ILCS 5/11(b) (2013).


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2) The plaintiff must be able to prove that the defendant breached the standard of care, exposed the plaintiff to an unreasonable risk of injury, and the plaintiff must have suffered that type of injury.
Today the Supreme Court of Canada granted the Plaintiff leave to appeal (permission to appeal). Clarity in this area of personal injury law will be welcomed by lawyers across Canada and I’ll be sure to report on this case once reasons for judgement are handed down.
When suing someone for damages as a result of a BC motor vehicle collision it is important to understand how our Courts establish who is at fault.
BC Courts must, in most circumstances, use the “but for” test. In the most basic terms, a driver has to exhibit some level of carelessness. From there a Judge (or Jury) must ask themselves if “but for the carelessness the collision would not have occurred“. If the answer is yes then the careless party must be found, at least partially, to blame for the accident. This week the BC Court of Appeal discussed this area of law.
In this week’s case (Skinner v. Fu) the Defendant was driving a vehicle on a well travelled BC highway and came to a stop because a dead animal was in his lane. It was dark and the Defendant remained stopped for a period of time. The speed limit was 90 kilometers per hour. He did not activate his brake lights or emergency flashers. The Plaintiff, approaching from the same direction of travel, failed to realize that the Defendant’s vehicle was stationary and this resulted in a rear-end collision.
First, the basic test for determining causation remains the “but for” test. This applies to multi-cause injuries. The plaintiff bears the burden of showing that “but for” the negligent act or omission of each defendant, the injury would not have occurred. Having done this, contributory negligence may be apportioned, as permitted by statute.
… In determining the issue of liability for the accident, I must determine whether the negligence of the defendant was the proximate cause or materially contributed to the occurrence of the collision.
 In my view the judge erred in the way he framed the analysis. “Proximate cause” or “effective cause” are sometimes confusing terms.
“Proximate cause” is a phrase ill-suited to the task of identifying culpable causes in negligence. It implies that the law recognizes only one cause and that this sole cause must be close in time and space to the event. As I have explained, these implications are not correct – every event has multiple historical factual causes. The phrase “proximate cause” is most often used in tort law synonymously with “remoteness”, that is, “to inject some degree of restraint on the potential reach of causation”: R. v. Goldhart, at para. 36. It suggests a limit on the scope of liability. There is also a doctrine of proximate cause in insurance law, where the term has been used to signify the main or dominant or effective cause of a loss, since the insurer has contracted to pay for the loss only if, or unless, it was caused by an event specified in the insurance policy. It must be noted that the term’s usefulness in insurance law has also been questioned: see C.C.R. Fishing Ltd. v. British Reserve Insurance Co.,  1 S.C.R. 814 at 823, 69 D.L.R. (4th) 112,  3 W.W.R. 501; Derksen v. 539938 Ontario Ltd., 2001 SCC 72,  3 S.C.R. 398 at para. 36, 205 D.L.R. (4th) 1.
 The judge’s use of the term “proximate cause” in this case, diverted the analysis from the correct approach, the “but for” test. The judge must have employed a last clear chance analysis when he used the term “proximate”. That term implies a finding of no liability based on a determination that the appellant could have entirely avoided the accident if only he had been more attentive to the road ahead of him. The judge found that the defendant was negligent. Indeed he could hardly have found otherwise. The respondent did create an unreasonable risk of harm by remaining stationary in the way he did.
 The judgment in Resurfice Corp. v. Hanke refines the test of causation and reminds us that the defendant’s breach of the standard of care need only be a cause of the plaintiff’s injury and not the sole cause (see also Athey v. Leonati,  3 S.C.R. 458). There may exist other causes that materially contributed to the injury, but that does not relieve the defendant of liability. In such circumstances, relief from liability follows only if the defendant’s breach of his standard of care did not materially contribute to the plaintiff’s injury. The analysis should be focused on the question: “but for” the defendant’s breach of the standard of care, would the plaintiff have suffered damage? Here the judge did use the term “materially contributed” at paragraph 9, as set out above, but I conclude that he used the term synonymously with “proximate cause”. I reach this conclusion because he did not analyze the facts consistently with the Athey material contribution test but rather in the proximate or only one cause analysis that was criticized in Chambers.
 In summary, it is my view that the judge erred by focusing his inquiry on the conduct of the appellant to the exclusion of the admitted negligence of the respondent. That inquiry properly was one of apportionment, but the judge neglected the essential underlying inquiry into the respondent’s negligence, and whether it was connected causally to the appellant’s injury (Resurfice at para. 23). The judge erred in failing to consider whether the respondent’s conduct created an unreasonable risk of harm and secondly, in failing to apply the “but for” analysis. If he had done so, he would have had to conclude that the respondent’s breach of the reasonable standard of care was a cause of the accident.
 This is not to say that there is anything wrong with the generally accepted rule that following drivers will usually be at fault for failing to avoid a collision with a vehicle that has stopped quickly in front (Ayers v. Singh, 85 B.C.A.C. 307,  B.C.J. No. 350). Normally a sudden stop does not create an unreasonable risk of harm. However, here the respondent’s act of remaining stationary, in the dark, on a well-traveled highway, where the speed limit was 90 kilometres per hour, without activating either brake lights or emergency flashers, did create an unreasonable risk of harm as that term was used by the Chief Justice in Lawrence.
 I would order a new trial because the necessary findings of fact that would enable this court to determine, and if necessary apportion, fault have not been made.
If you are thinking of bringing a claim for compensation for personal injuries you should first ask yourself “did the other party do something wrong?”. From there you need to ask “but for that wrongful act, the injury would not have occurred?“. If the answer is yes then you have a theory on which to advance your case.

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The United States Patent and Trademark Office (USPTO) has an interesting history of what might informally be called non-patent patents. That is, legal provisions, or grants, that protect patentable subject matter, but with important limitations as compared to the exclusive rights afforded in a regular U.S. letters patent.
One such non-patent patent is the long-defunct patent caveat, instituted by the U.S. Patent Act of 1836. A caveat was a legal document similar to a patent application with a description of any “new art, machine, or improvement thereof” (U.S. Patent Act of 1836 § 12) and drawings, sans claims and examination. It was a notice (and cost $20 to file) to the Patent Office of an inventor’s intent to file a patent at a later date—“praying protection of his right till he shall have matured his invention” (U.S. Patent Act of 1836 § 12)— and had a shelf-life of one year, but could be renewed. A caveat was “filed in the confidential archives of the office, and preserved in secrecy” (U.S. Patent Act of 1836 § 12).
A modern version of the patent caveat is the provisional patent. Beginning June 8, 1995, the USPTO provided inventors with a provisional application (35 U.S.C. §111[b]), which requires the filing of a description (no claims required) and drawings where necessary for the understanding of the subject matter sought to be patented (35 U.S.C. § 113), to establish a filing date. A provisional application expires one year from the original filing date, and does not mature into an issued patent unless the inventor files a nonprovisional application before the expiration date. A provisional patent application is not examined and, unlike its predecessor, the caveat, the twelve-month period cannot be extended. A provisional application provides the means to establish an early filing date in a later-filed nonprovisional patent application filed under 35 U.S.C. §111(a) and does not count against the twenty-year patent term. It also allows an applicant to use the term “Patent Pending” when describing his or her invention.
The USPTO Defensive Publication Program, which provided for the publication of the abstract of the technical disclosure of a pending application if the applicant waived his or her rights to an enforceable patent, was available April 1968 to May 1985. A defensive publication offered limited protection by preventing others from later patenting the same invention, design, or plant. Such a publication was available to the public, and the applicant provisionally abandoned the application, retaining rights to an interference for a limited period of five years from the earliest effective U.S. filing date (MPEP 711.06).


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Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and the Ph.D. degree in Computer Science from the University of Orsay in 1989. He then worked at SRI International and INRIA Sophia-Antipolis as a Computer Scientist. He joined EPFL in 1996 where he is now a Professor in the School of Computer and Communication Science and heads the Computer Vision Laboratory.
His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. He has (co)authored over 300 publications in refereed journals and conferences. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded two spinoff companies (Pix4D and PlayfulVision).
The research activities of the Computer Vision Laboratory focus on shape and motion recovery from images, object and people detection and tracking in video sequences, and analysis of brain microscopy image-stacks. CVLab also provides undergraduate and graduate teaching and performs technology transfer to both established and start up companies.
Pascal Fua's research is sponsored by the Swiss National Science Foundation, the CTI, the European Union including a senior ERC grant, and several industrial partners.
R. Achanta, A. Shaj, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk.
A. Maksai / P. Fua (Dir.) : Training Algorithms for Multiple Object Tracking. Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9203.
T. Bagautdinov; C. Wu; J. Saragih; Y. Sheikh; P. Fua : Modeling Facial Geometry using Compositional VAEs. 2018-06-18. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 18-22, 2018.
B. Tekin; S. N. Sinha; P. Fua : Real-Time Seamless Single Shot 6D Object Pose Prediction. 2018-06-18. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018.
J. Bednarík; M. Salzmann; P. Fua : Learning to Reconstruct Texture-less Deformable Surfaces from a Single View. 2018-03-23. International Conference on 3D Vision, Verona, Italy, September 5-8, 2018.
I. Katiricioglu; B. Tekin; M. Salzmann; V. Lepetit; P. Fua : Learning Latent Representations of 3D Human Pose with Deep Neural Networks; International Journal of Computer Vision. 2018-01-31. DOI : 10.1007/s11263-018-1066-6.
Y. Ono; E. Trulls Fortuny; P. Fua; K. M. Yi : LF-Net: Learning Local Features from Images. 2018. Neural Information Processing Systems (NIPS), Montréal Canada.
R. Bermudez-Chancon; P. Marquez-Neila; M. Salzmann; P. Fua : A Domain-Adaptive Two-Stream U-Net For Electron Microscopy Image Segmentation. 2018-01-01. International Symposium on Biomedical Imaging (ISBI), Washington, DC, Apr 04-07, 2018. p. 400-404.
J. Bednarik; P. Fua; M. Salzmann : Learning to Reconstruct Texture-less Deformable Surfaces from a Single View. 2018-01-01. 6th International Conference on 3D Vision (3DV), Verona, ITALY, Sep 05-08, 2018. p. 606-615. DOI : 10.1109/3DV.2018.00075.
C. Cali; M. Wawrzyniak; C. Becker; B. Maco; M. Cantoni et al. : The effects of aging on neuropil structure in mouse somatosensory cortex-A 3D electron microscopy analysis of layer 1; PLOS ONE. 2018. DOI : 10.1371/journal.pone.0198131.
A. Crivellaro; M. Rad; Y. Verdie; K. Yi; P. Fua et al. : Robust 3D Object Tracking from Monocular Images Using Stable Parts; Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2018. DOI : 10.1109/TPAMI.2017.2708711.
F. Saleh; M. Aliakbarian; M. Salzmann; L. Petersson; J. Alvarez et al. : Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation; Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2018. DOI : 10.1109/TPAMI.2017.2713785.
M. Harandi; M. Salzmann; R. Hartley : Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods; Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2018. DOI : 10.1109/TPAMI.2017.2655048.
V. Paradiso; A. Crivellaro; K. Amgarou; N. de Lanaute; P. Fua et al. : A versatile calibration procedure for portable coded aperture gamma cameras and RGB-D sensors; NUCLEAR INSTRUMENTS AND METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT. 2018. DOI : 10.1016/j.nima.2017.12.065.
B. Tekin / P. Fua; V. Lepetit (Dir.) : Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8753.
M. Kozinski; A. J. Mosinska; M. Salzmann; P. Fua : Learning to Segment 3D Linear Structures Using Only 2D Annotations. 2018. MICCAI, Granada, Spain, September 16-20, 2018.
A. Tkach / M. Pauly; A. Tagliasacchi (Dir.) : Real-Time Generative Hand Modeling and Tracking. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8573.
Z. Dang; K. M. Yi; Y. Hu; F. Wang; P. Fua et al. : Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses. 2018. European Conference on Computer Vision, Munich, Germany, September 8-14, 2018.
H. Rhodin; M. Salzmann; P. Fua : Unsupervised Geometry-Aware Representation Learning for 3D Human Pose Estimation. 2018. European Conference on Computer Vision (ECCV).
A. J. Mosinska; P. Marquez Neila; M. Kozinski; P. Fua : Beyond the Pixel-Wise Loss for Topology-Aware Delineation. 2018. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 18-22, 2018.
R. Bermúdez Chacón; P. Marquez Neila; M. Salzmann; P. Fua : A Domain-Adaptive Two-Stream U-Net for Electron Microscopy Image Segmentation. 2018. International Symposium of Biomedical Imaging (ISBI), Washington DC, USA, April 4-7, 2018.
K. Konyushkova; J. Uijlings; C. H. Lampert; V. Ferrari : Learning Intelligent Dialogs for Bounding Box Annotation. 2018. Conference on Computer Vision and Pattern Recognition (CVPR).
H. Rhodin; J. Spörri; I. Katircioglu; V. Constantin; F. Meyer et al. : Learning Monocular 3D Human Pose Estimation from Multi-view Images. 2018. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA.
A. Rozantsev; M. Salzmann; P. Fua : Residual Parameter Transfer for Deep Domain Adaptation. 2018. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 18-22, 2018.
K. M. Yi; E. Trulls Fortuny; Y. Ono; V. Lepetit; M. Salzmann et al. : Learning to Find Good Correspondences. 2018. Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 18-22, 2018.
A. Rozantsev; M. Salzmann; P. Fua : Beyond Sharing Weights for Deep Domain Adaptation; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018. DOI : 10.1109/TPAMI.2018.2814042.
P. Marquez Neila; M. Salzmann; P. Fua : Imposing Hard Constraints on Deep Networks: Promises and Limitations. 2017. CVPR Workshop on Negative Results in Computer Vision, Hawaii, HI, 2017.
P. Fua; V. Lepetit; A. Rozantsev; B. Tekin ; Method, System and Device for Direct Prediction of 3D Body Poses from Motion Compensated Sequence. US2017316578 . 2017.
M. A. Pinheiro; J. Kybic; P. Fua : Geometric Graph Matching Using Monte Carlo Tree Search; Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2017. DOI : 10.1109/Tpami.2016.2636200.
T. Kurmann; P. Marquez Neila; X. Du; P. Fua; D. Stoyanov et al. : Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery. 2017. International Conference on Medical Image Computing and Computer-Assisted Intervention (MCCAI), Quebec, Canada.
A. E. Ichim / M. Pauly (Dir.) : Physics-based Reconstruction and Animation of Humans. Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7880.
E. Oveisi; A. Letouzey; D. Alexander; Q. Jeangros; R. Schäublin et al. : Tilt-less 3-D electron imaging and reconstruction of complex curvilinear structures; Scientific reports. 2017. DOI : 10.1038/s41598-017-07537-6.
E. Oveisi; A. Letouzey; S. De Zanet; G. Lucas; M. Cantoni et al. : Stereo-vision three-dimensional reconstruction of curvilinear structures imaged with a TEM; Ultramicroscopy. 2017. DOI : 10.1016/j.ultramic.2017.08.010.
B. Tekin; P. Marquez Neila; M. Salzmann; P. Fua : Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation. 2017. International Conference on Computer Vision (ICCV).
E. Oveisi; A. Letouzey; P. Fua; C. Hébert : Three-dimensional electron imaging and reconstruction of dislocations from a single acquisition. 2017. Microscopy Conference, Lausanne, Switzerland.
E. Oveisi; S. De Zanet; P. Fua; C. Hébert : Computer Vision Techniques Applied to the Reconstruction of the 3-D Structure of Dislocations. 2017. Microscopy & Microanalysis, St Louis, MO, USA, 2017. p. 102-103. DOI : 10.1017/S1431927617001192.
A. J. Mosinska; J. Tarnawski; P. Fua : Active Learning and Proofreading for Delineation of Curvilinear Structures. 2017. MICCAI, Quebec City, Canada, September 10-14, 2017.
D. Mandl; K. M. Yi; P. Mohr; P. Roth; P. Fua et al. : Learning Lightprobes for Mixed Reality Illumination. 2017. International Symposium on Mixed and Augmented Reality (ISMAR), Nantes, France, October 9–13, 2017.
P. Baqué; F. Fleuret; P. Fua : Multi-Modal Mean-Fields via Cardinality-Based Clamping. 2017. CVPR 2017, Honolulu, Hawaii, USA, July 21-26, 2017. p. 4303-4312. DOI : 10.1109/Cvpr.2017.458.
A. Rozantsev / P. Fua; V. Lepetit (Dir.) : Vision-based detection of aircrafts and UAVs. Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7589.
A. Rozantsev; S. Sinha; D. Dey; P. Fua : Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras. 2017. Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, July, 2017. p. 2482-2491. DOI : 10.1109/Cvpr.2017.266.
P. Glowacki; M. Pinheiro; A. Mosinska; E. Türetken; D. Lebrecht et al. : Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency; Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2017. DOI : 10.1109/TPAMI.2017.2680444.
E. Türetken; X. Wang; C. J. Becker; C. Haubold; P. Fua : Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences; Transactions on Medical Imaging (TMI). 2017. DOI : 10.1109/Tmi.2016.2640859.
A. Rozantsev; V. Lepetit; P. Fua : Detecting Flying Objects using a Single Moving Camera; Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2017. DOI : 10.1109/TPAMI.2016.2564408.
E. Oveisi; A. Letouzey; P. Fua; C. Hébert : Three-dimensional electron imaging of dislocations from a single sample tilt. 2016. The 16th European Microscopy Congress 2016, Lyon, France, 28 August - 2 September, 2016. DOI : 10.1002/9783527808465.
B. Fan; Q. Kong; W. Sui; Z. Wang; X. Wang et al. : Do We Need Binary Features for 3D Reconstruction?. 2016. Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, June, 2016. DOI : 10.1109/Cvprw.2016.144.
L. Smith; D. Nevins; N. T. Dat; P. Fua : Measuring the accuracy of softball impact simulations; Sports Engineering. 2016. DOI : 10.1007/s12283-016-0210-1.
A. Sironi / P. Fua; V. Lepetit (Dir.) : Multiscale Centerline Extraction Based on Regression and Projection onto the Set of Elongated Structures. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7241.
A. Crivellaro / P. Fua; V. Lepetit (Dir.) : Robust 3D Object Pose Estimation and Tracking from Monocular Images in Industrial Environments. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7282.
D. Robben; E. Tueretken; S. Sunaert; V. Thijs; G. Wilms et al. : Simultaneous segmentation and anatomical labeling of the cerebral vasculature; Medical Image Analysis. 2016. DOI : 10.1016/j.media.2016.03.006.
K. M. Yi; E. Trulls Fortuny; V. Lepetit; P. Fua : LIFT: Learned Invariant Feature Transform. 2016. European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, October 8-16, 2016. p. 467-483. DOI : 10.1007/978-3-319-46466-4_28.
R. Bermúdez-Chacón; C. Becker; M. Salzmann; P. Fua : Scalable Unsupervised Domain Adaptation for Electron Microscopy. 2016. MICCAI 2016, Athens, Greece, October 17-21, 2016.
B. Tekin; I. Katircioglu; M. Salzmann; V. Lepetit; P. Fua : Structured Prediction of 3D Human Pose with Deep Neural Networks. 2016. British Machine Vision Conference (BMVC), York, UK, September 19-22, 2016.
V. Belagiannis; X. Wang; H. B. Ben Shitrit; K. Hashimoto; R. Stauder et al. : Parsing human skeletons in an operating room; Machine Vision and Applications. 2016. DOI : 10.1007/s00138-016-0792-4.
C. J. Becker / P. Fua (Dir.) : Towards Scalable Segmentation of 3D Image Stacks. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6937.
J. VanHaaren; H. Ben Shitrit; J. Davis; P. Fua : Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques. 2016. Conference on Knowledge Discovery and Data Mining, San Francisco, CA, August, 2016.
P. Baqué; T. Bagautdinov; F. Fleuret; P. Fua : Principled Parallel Mean-Field Inference for Discrete Random Fields. 2016. Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition.
K. M. Yi; Y. Verdie; P. Fua; V. Lepetit : Learning to Assign Orientations to Feature Points. 2016. Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, USA, June 26-July 1, 2016.
H. Altwaijry; E. Trulls; J. Hays; P. Fua; S. Belongie : Learning to Match Aerial Images with Deep Attentive Architectures. 2016. Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, June 27-30, 2016.
B. Tekin; A. Rozantsev; V. Lepetit; P. Fua : Direct Prediction of 3D Body Poses from Motion Compensated Sequences. 2016. Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, USA, June 26-July 1, 2016.
T. D. Ngo / P. Fua (Dir.) : Template-based Monocular 3-D Shape Reconstruction And Tracking Using Laplacian Meshes. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6891.
E. Türetken; X. Wang; C. J. Becker; C. Haubold; P. Fua : Globally Optimal Cell Tracking using Integer Programming. 2016.
P. B. Baqué; T. Bagautdinov; F. Fleuret; P. Fua : Principled Parallel Mean-Field Inference for Discrete Random Fields. 2016. Computer Vision and Pattern Recognition (CVPR), Las Vegas.
O. Pertz; L. Fusco; R. Lefort; K. C. Smith; F. Benmansour et al. : Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling; Journal of Cell Biology. 2016. DOI : 10.1083/jcb.201506018.
A. Sironi; E. Türetken; V. Lepetit; P. Fua : Multiscale Centerline Detection; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016. DOI : 10.1109/Tpami.2015.2462363.
E. Türetken; F. Benmansour; B. Andres; P. R. Glowacki; H. Pfister et al. : Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016. DOI : 10.1109/Tpami.2016.2519025.
E. Turetken; P. Fua; F. Fleuret; X. Wang ; Systems and methods for tracking interacting objects. US9794525 ; US2015281655 . 2015.
B. Stenger; N. Ukita; Y. Sato; P. Fua; D. Fleet : Special Section: Pose & Gesture; Computer Vision And Image Understanding. 2015. DOI : 10.1016/j.cviu.2015.10.011.
N. Bagdadioglu; M. Finger; F. Oguz : Introduction to the special issue on Network Industries in Turkey; Utilities Policy. 2015. DOI : 10.1016/j.jup.2015.06.004.
M. E. Khan; P. B. Baqué; F. Fleuret; P. Fua : Kullback-Leibler Proximal Variational Inference. 2015. Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, December 9, 2015.
E. Khan; P. Baqué; F. Fleuret; P. Fua : Kullback-Leibler Proximal Variational Inference. 2015. Proceedings of the international conference on Neural Information Processing Systems.
E. Simo-Serra; E. Trulls; L. Ferraz; I. Kokkinos; P. Fua et al. : Discriminative Learning of Deep Convolutional Feature Point Descriptors. 2015. International Conference on Computer Vision, Santiago, Chile, December 13-16, 2015.
A. Sironi; V. Lepetit; P. Fua : Projection onto the Manifold of Elongated Structures for Accurate Extraction. 2015. International Conference on Computer Vision (ICCV), Santiago, Chile, December 11-18, 2015.
T. D. Ngo; S. Park; A. A. Jorstad; A. Crivellaro; C. Yoo et al. : Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture. 2015. International Conference on Computer Vision (ICCV), Santiago, Chile, December 13-16, 2015.
X. Sun / P. Fua (Dir.) : Localizing Polygonal Objects in Man-Made Environments. Lausanne, EPFL, 2015. DOI : 10.5075/epfl-thesis-6622.
S. Magnenat; T. D. Ngo; F. Zund; M. Ryffel; G. Noris et al. : Live Texturing of Augmented Reality Characters from Colored Drawings; IEEE Transactions on Visualization and Computer Graphics. 2015. DOI : 10.1109/Tvcg.2015.2459871.
X. Wang / P. Fua (Dir.) : Tracking Interacting Objects in Image Sequences. Lausanne, EPFL, 2015. DOI : 10.5075/epfl-thesis-6632.
Y. Verdie; K. M. Yi; P. Fua; V. Lepetit : TILDE: A Temporally Invariant Learned DEtector. 2015. Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, USA.
P. Fua; G. Knott : Modeling Brain Circuitry over a Wide Range of Scales; Frontiers in Neuroanatomy. 2015. DOI : 10.3389/fnana.7015.00042.
A. Rozantsev; V. Lepetit; P. Fua : Flying Objects Detection from a Single Moving Camera. 2015. Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, USA, June 7-12, 2015. p. 4128-4136. DOI : 10.1109/CVPR.2015.7299040.
T. Bagautdinov; F. Fleuret; P. Fua : Probability Occupancy Maps for Occluded Depth Images. 2015. Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, USA, June 7-12, 2015.
E. Türetken; X. Wang; C. J. Becker; P. Fua : Detecting and Tracking Cells using Network Flow Programming. 2015.
C. J. Becker; C. M. Christoudias; P. Fua : Domain Adaptation for Microscopy Imaging; IEEE Transactions on Medical Imaging. 2015. DOI : 10.1109/Tmi.2014.2376872.
A. Jorstad; B. Nigro; C. Cali; M. Wawrzyniak; P. Fua et al. : NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks; Neuroinformatics. 2015. DOI : 10.1007/s12021-014-9242-5.
A. Sironi; B. Tekin; R. Rigamonti; V. Lepetit; P. Fua : Learning Separable Filters; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI : 10.1109/Tpami.2014.2343229.
E. Serradell; M. Pinheiro; R. Sznitman; J. Kybic; F. Moreno-Noguer et al. : Non-Rigid Graph Registration using Active Testing Search; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI : 10.1109/TPAMI.2014.2343235.
P. Fua; G. Knott; R. Sznitman; A. Lucchi ; Efficient scanning for em based target localization. WO2014001157 ; US8588509 . 2014.
B. Maco; A. Holtmaat; A. Jorstad; P. Fua; G. Knott : Correlative In Vivo 2-Photon Beam Scanning Electron Microscopy: 3D Analysis of Neuronal Ultrastructure; Correlative Light And Electron Microscopy II; San Diego: Elsevier Academic Press Inc, 2014. p. 339-361.
X. Wang; E. Turetken; F. Fleuret; P. Fua : Tracking Interacting Objects Optimally Using Integer Programming. 2014. Proceedings of the European Conference on Computer Vision.
A. Jorstad; P. Fua : Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces. 2014. European Conference on Computer Vision (ECCV) Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, Zurich, Switzerland, September 6-12, 2014. p. 367-379. DOI : 10.1007/978-3-319-16220-1_26.
A. Greenblum; R. Sznitman; P. Fua; P. E. Arratia; M. Oren et al. : Dendritic tree extraction from noisy maximum intensity projection images in C-elegans; Biomedical Engineering Online. 2014. DOI : 10.1186/1475-925X-13-74.
T. P. Trzciński / P. Fua; V. Lepetit (Dir.) : Learning and Matching Binary Local Feature Descriptors. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6226.
R. Rigamonti; V. Lepetit; P. Fua : Beyond KernelBoost. 2014.
V. Belagiannis; X. Wang; B. Schiele; P. Fua; S. Ilic et al. : Multiple Human Pose Estimation with Temporally Consistent 3D Pictorial Structures. 2014. European Conference on Computer Vision, ChaLearn Looking at People Workshop, Zurich, Switzerland, September 6-12, 2014. p. 742-754. DOI : 10.1007/978-3-319-16178-5_52.
X. Sun; C. M. Christoudias; P. Fua : Free-Shape Polygonal Object Localization. 2014. European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sep 6-12, 2014. p. 317-332.
D. Robben; E. Türetken; S. Sunaert; V. Thijs; G. Wilms et al. : Simultaneous Segmentation and Anatomical Labeling of the Cerebral Vasculature. 2014. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Boston, Massachusetts, USA, Sep. 14-19, 2014.
R. Sznitman; C. J. Becker; P. Fua : Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery. 2014. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, Massachusetts, USA, September 14-18, 2014.
B. Fan; Q. Kong; T. Trzcinski; Z. Wang; C. Pan et al. : Receptive Fields Selection for Binary Feature Description; IEEE Transactions on Image Processing. 2014. DOI : 10.1109/TIP.2014.2317981.
P. Glowacki; M. Pinheiro; E. Türetken; R. Sznitman; D. Lebrecht et al. : Reconstructing Evolving Tree Structures in Time Lapse Sequences. 2014. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014.
A. Sironi; V. Lepetit; P. Fua : Multiscale Centerline Detection by Learning a Scale-Space Distance Transform. 2014. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 24-27, 2014.
R. Rigamonti; V. Lepetit; G. González; E. Türetken; F. Benmansour et al. : On the Relevance of Sparsity for Image Classification; Computer Vision and Image Understanding. 2014. DOI : 10.1016/j.cviu.2014.03.009.
X. Sun; C. M. Christoudias; V. Lepetit; P. Fua : Real-time landing place assessment in man-made environments; Machine Vision And Applications. 2014. DOI : 10.1007/s00138-013-0560-7.
H. B. Ben Shitrit / P. Fua; F. Fleuret (Dir.) : Identity-Preserving Multiple People Tracking. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6035.
R. Sznitman; A. Greenblum; P. Fua; P. Arratia; J. Sznitman : Caenorhabditis elegant segmentation using texture-based models for motility phenotyping; IEEE Transactions on Biomedical Engineering. 2014. DOI : 10.1109/Tbme.2014.2298612.
R. Rigamonti / P. Fua; V. Lepetit (Dir.) : Better features for image categorization and segmentation. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6031.
C. Dubout / F. Fleuret (Dir.) : Object Classification and Detection in High Dimensional Feature Space. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6043.
F. Fleuret; H. Ben Shitrit; P. Fua : Re-Identification for Improved People Tracking; Person Re-Identification; Springer, 2014. p. 309-330.
H. Ben Shitrit; J. Berclaz; F. Fleuret; P. Fua : Multi-Commodity Network Flow for Tracking Multiple People; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014. DOI : 10.1109/Tpami.2013.210.
B. Tekin : Separable Filter Learning with Tensor Decomposition ; 2013.
F. Fleuret; J. Berclaz; E. Tueretken; P. Fua; F. Fleuret ; Method and apparatus for multiple object tracking with k-shortest paths. US8615107 ; US2013177200 . 2013.
B. Tekin : Learning Separable Filters with Shared Parts ; 2013.
R. Sznitman; C. Becker; F. Fleuret; P. Fua : Fast Object Detection with Entropy-Driven Evaluation. 2013. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, June 23-28, 2013. DOI : 10.1109/Cvpr.2013.420.
P. F. Felzenszwalb; D. A. Forsyth; P. Fua; T. E. Boult : TPAMI CVPR Special Section; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013. DOI : 10.1109/TPAMI.2013.208.
S. Cuendet / P. Dillenbourg (Dir.) : Tangible Interfaces for Learning. Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5931.
A. Lopes; J. Florêncio; H. Ben Shitrit; P. Fua; S. Fonseca : Tracking Multiple Handball Players using Multi-Commodity Network Flow for Assessing Tactical Behavior. 2013. Scientific Conference Women and Handball: Scientific and Practical Approaches, Vienna, Austria, November, 2013.
E. Türetken; C. Becker; P. Glowacki; F. Benmansour; P. Fua : Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery using Multi-Directional Oriented Flux. 2013. International Conference on Computer Vision (ICCV), Sydney, Australia, December, 2013.
E. Türetken / P. Fua (Dir.) : Automated Reconstruction of Curvilinear Networks from 2D and 3D Imagery. Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5933.
V. Lepetit; P. Fua : Keypoint Recognition using Random Forests and Random Ferns; Decision Forests for Computer Vision and Medical Image Analysis; Springer, 2013. p. 111-124.
C. J. Becker; C. M. Christoudias; P. Fua : Non-Linear Domain Adaptation with Boosting. 2013. Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA, December 5-8, 2013.
A. Lucchi / P. Fua (Dir.) : Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images. Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5836.
C. J. Becker; R. Rigamonti; V. Lepetit; P. Fua : Supervised Feature Learning for Curvilinear Structure Segmentation. 2013. International Conference on Medical Image Computing And Computer Assisted Intervention (MICCAI), Nagoya, Japan, September 22-26, 2013. p. 526-533.
M. Zervos; H. BenShitrit; F. Fleuret; P. Fua : Facial Descriptors for Identity-Preserving Multiple People Tracking. 2013.
M. A. Pinheiro; R. Sznitman; M. Serradell; J. Kybic; F. Moreno-Nogue et al. : Active Testing Search for Point Cloud Matching. 2013. International Conference on Information Processing in Medical Imaging (IPMI), Asilomar, California, USA, June 28, 2013.
R. Sznitman; A. Lucchi; M. Cantoni; G. Knott; P. Fua : Flash Scanning Electron Microscopy. 2013. 16th Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, September 22-26, 2013.
H. Ben Shitrit; J. Berclaz; F. Fleuret; P. Fua ; Tracklet-based Multi-Commodity Network Flow for Tracking Multiple People. EP2780871 ; EP2780871 ; WO2013072401 ; WO2013072401 . 2013.
N. Frémaux / W. Gerstner (Dir.) : Models of Reward-Modulated Spike-Timing-Dependent Plasticity. Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5683.
T. Trzcinski; C. M. Christoudias; P. Fua; V. Lepetit : Boosting Binary Keypoint Descriptors. 2013. Computer Vision and Pattern Recognition (CVPR), Portland, USA. DOI : 10.1109/Cvpr.2013.370.
E. Türetken; F. Benmansour; B. Andres; H. Pfister; P. Fua : Reconstructing Loopy Curvilinear Structures Using Integer Programming. 2013. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, June, 2013. DOI : 10.1109/Cvpr.2013.238.
A. Rozantsev; C. M. Christoudias; V. Lepetit; P. Fua : Detection of Aircrafts on a Collision Course using Spatio-Temporal HOG. 2013.
R. Sznitman; C. J. Becker; F. Fleuret; P. Fua : Fast Object Detection with Entropy-Driven Evaluation. 2013. Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA.
R. Rigamonti; A. Sironi; V. Lepetit; P. Fua : Learning Separable Filters. 2013. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, June 25-27, 2013. DOI : 10.1109/Cvpr.2013.355.
R. Sznitman; A. Lucchi; P. I. Frazier; B. M. Jedynak; P. Fua : An Optimal Policy for Target Localization with Application to Electron Microscopy. 2013. International Conference on Machine Learning (ICML), Atlanta, GA, USA, June 16-21, 2013.
M. Zervos : Multi-camera face detection and recognition applied to people tracking ; 2013.
C. J. Becker; K. Ali; G. Knott; P. Fua : Learning Context Cues for Synapse Segmentation; IEEE Transactions on Medical Imaging. 2013. DOI : 10.1109/Tmi.2013.2267747.
F. Benmansour; E. Türetken; P. Fua : Tubular Geodesics using Oriented Flux: An ITK Implementation; Insight Journal. 2013.
C. J. Becker; R. Rigamonti; V. Lepetit; P. Fua : KernelBoost: Supervised Learning of Image Features For Classification. 2013.
E. Türetken; F. Benmansour; P. Fua : Semi-Automated Reconstruction of Curvilinear Structures in Noisy 2D images and 3D image stacks. 2013.
H. Ben Shitrit; M. Raca; F. Fleuret; P. Fua : Tracking Multiple Players using a Single Camera; Machine Vision and Applications. 2013.
F. Moreno Noguer; P. Fua : Stochastic Exploration of Ambiguities for Non-Rigid Shape Recovery; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013. DOI : 10.1109/Tpami.2012.102.
Y. LI; N. Snavely; D. Huttenlocher; P. Fua : Worldwide Pose Estimation Using 3D Point Clouds. 2012. European Conference on Computer Vision (ECCV), Florence, Italy, October, 2012.
T. Trzcinski; C. M. Christoudias; V. Lepetit; P. Fua : Learning Image Descriptors with the Boosting-Trick. 2012. NIPS, Lake Tahoe, CA, USA, 2012.
M. Zervos : Real time multi-object tracking using multiple cameras ; 2012.
S. Mateo; C. Blum; P. Fua; E. Türetken : Hybrid Algorithms for the Minimum-Weight Rooted Arborescence Problem. 2012. International Conference on Swarm Intelligence, Brussels, Belgium, September, 2012.
V. H. Ablavsky; C. J. Becker; P. Fua : Transfer Learning by Sharing Support Vectors. 2012.
R. Rigamonti; V. Lepetit; G. González Serrano; E. Türetken; F. Benmansour et al. : On the Relevance of Sparsity for Image Classification. 2012.
T. Trzcinski; V. Lepetit; P. Fua : Thick Boundaries in Binary Space and Their Influence on Nearest-Neighbor Search; Pattern Recognition Letters. 2012. DOI : 10.1016/j.patrec.2012.08.006.
A. Varol / P. Fua (Dir.) : Resolving Ambiguities in Monocular 3D Reconstruction of Deformable Surfaces. Lausanne, EPFL, 2012. DOI : 10.5075/epfl-thesis-5458.
R. Sznitman; K. Ali; R. Richa; R. Taylor; G. Hager et al. : Data-Driven Visual Tracking in Retinal Microsurgery. 2012. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, October, 2012.
R. Sznitman; A. Lucchi; N. Pjescic-Emedi; G. Knott; P. Fua : Efficient Scanning for EM Based Target Localization. 2012. 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Nice, France, October, 2012.
R. Rigamonti; V. Lepetit; P. Fua : Learning Separable Filters. 2012.
C. J. Becker; K. Ali; G. Knott; P. Fua : Learning Context Cues for Synapse Segmentation in EM Volumes. 2012. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).
S. Hinterstoisser; C. Cagniart; S. Ilic; P. Sturm; N. Navab et al. : Gradient Response Maps for Real-Time Detection of Textureless Objects; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012. DOI : 10.1109/TPAMI.2011.206.
E. Serradell; P. Glowacki; J. Kybic; F. Moreno-Noguer; P. Fua : Robust Non-Rigid Registration of 2D and 3D Graphs. 2012. Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 2012.
A. Varol; M. Salzmann; P. Fua; R. Urtasun : A Constrained Latent Variable Model. 2012. IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, June 16-21, 2012.
C. Strecha; O. Küng; P. Fua : AUTOMATIC MAPPING FROM ULTRA-LIGHT UAV IMAGERY. 2012. EuroCOW 2012, Barceloa, Spain, February 8-10, 2012.
K. Ali / P. Fua; F. Fleuret (Dir.) : Learning to Detect Objects with Minimal Supervision. Lausanne, EPFL, 2012. DOI : 10.5075/epfl-thesis-5310.
M. Calonder; V. Lepetit; M. Özuysal; T. Trzcinski; C. Strecha et al. : BRIEF: Computing a Local Binary Descriptor Very Fast; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012. DOI : 10.1109/TPAMI.2011.222.
K. Ali; F. Fleuret; D. Hasler; P. Fua : A Real-Time Deformable Detector; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012. DOI : 10.1109/TPAMI.2011.117.
A. Varol; A. Shaji; M. Salzmann; P. Fua : Monocular 3D Reconstruction of Locally Textured Surfaces; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012. DOI : 10.1109/TPAMI.2011.196.
G. Gonzalez; L. Fusco; R. Lefort; F. Benmansour; P. Fua et al. : Automated Quantification of Morphodynamics for High-Throughput Live Cell Imaging Datasets. 2011. 1st International SystemsX.ch Conference on Systems Biology.
L. Fusco; K. C. Smith; F. Benmansour; R. Lefort; F. Fleuret et al. : Morphodynamic profiling to explore spatio-temporal signaling networks regulating neurite outgrowth. 2011. 1st International SystemsX.ch Conference on Systems Biology.
O. Küng; C. Strecha; P. Fua; D. Gurdan; M. Achtelik et al. : Simplified Building Models Extraction From Ultra-Light UAV Imagery. 2011. UAV-g 2011 - Unmanned Aerial Vehicle in Geomatics, Zürich, CH, September 14-16, 2011.
A. Lucchi; Y. Li; X. Boix Bosch; K. Smith; P. Fua : Are Spatial and Global Constraints Really Necessary for Segmentation?. 2011. IEEE International Conference on Computer Vision (ICCV), Barcelona. p. 9-16.
G. Roig Noguera; X. Boix Bosch; H. Ben Shitrit; P. Fua : Conditional Random Fields for Multi-Camera Object Detection. 2011. International Conference on Computer Vision, Barcelona, Spain, November 6-13, 2011.
H. Ben Shitrit; J. Berclaz; F. Fleuret; P. Fua : Tracking Multiple People under Global Appearance Constraints. 2011. International Conference on Computer Vision, Barcelona, Spain, November 6-13, 2011. p. 137-144.
H. Ben Shitrit; J. Berclaz; F. Fleuret; P. Fua : Tracking Multiple Objects under Global Appearance Constraints. 2011. Proceedings of the IEEE International Conference on Computer Vision. p. 137-144.
R. Rigamonti; E. Türetken; G. González Serrano; P. Fua; V. Lepetit : Filter Learning for Linear Structure Segmentation. 2011.
E. Türetken; G. González Serrano; C. Blum; P. Fua : Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors; Neuroinformatics. 2011. DOI : 10.1007/s12021-011-9122-1.
G. González Serrano / P. Fua; F. Fleuret (Dir.) : Automated Detection and Delineation of Tree Structures in Noisy Micrographs. Lausanne, EPFL, 2011. DOI : 10.5075/epfl-thesis-5065.
S. Hinterstoisser; V. Lepetit; S. Benhimane; P. Fua; N. Navab : Learning Real-Time Perspective Patch Rectification; International Journal of Computer Vision. 2011. DOI : 10.1007/s11263-010-0379-x.
A. Fossati; P. Schönmann; P. Fua : Real-time vehicle tracking for driving assistance; Machine Vision and Applications. 2011. DOI : 10.1007/s00138-009-0243-6.
J. Sanchez-Riera; J. O. M. Östlund; P. Fua; F. Moreno Noguer : Simultaneous Pose, Correspondence and Non-Rigid Shape. 2010. Conference on Computer Vision and Pattern Recognition.
M. Calonder; V. Lepetit; P. Fua : Pareto-optimal Dictionaries for Signatures. 2010. Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, Jun 13-18, 2010. p. 3011-3018.
G. Gonzalez; E. Türetken; F. Fleuret; P. Fua : Delineating Trees in Noisy 2D Images and 3D Image Stacks. 2010. International Conference on Computer Vision and Pattern Recognition. p. 2799-2806.
E. Tola; A. Fossati; C. Strecha; P. Fua : Large occlusion completion using normal maps. 2010. Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010.
A. Lucchi; K. Smith; R. Achanta; G. Knott; P. Fua : Supervoxel-Based Segmentation of EM Image Stacks with Learned Shape Features. 2010.
A. Varol; A. Shaji; M. Salzmann; P. Fua : Monocular 3D Reconstruction of Locally Textured Surfaces. 2010.
M. Salzmann; P. Fua : Deformable Surface 3D Reconstruction from Monocular Images ; Morgan-Claypool.
M. Calonder / P. Fua (Dir.) : Robust, High-Speed Interest Point Matching for Real-Time Applications. Lausanne, EPFL, 2010. DOI : 10.5075/epfl-thesis-4868.
P. Fua; A. Varol; R. Urtasun; M. Salzmann : Least-Squares Minimization Under Constraints. 2010.
E. Serradell; M. Özuysal; V. Lepetit; P. Fua; F. Moreno-Noguer : Combining Geometric and Appearance Priors for Robust Homography Estimation. 2010.
F. Moreno-Noguer; J. Porta; P. Fua : Exploring Ambiguities for Monocular Non-Rigid Shape Estimation. 2010. European Conference on Computer Vision, Heraklion, Greece, September, 2010.
E. Tola / P. Fua (Dir.) : DAISY: A Fast Descriptor for Dense Wide Baseline Stereo and Multiview Reconstruction. Lausanne, EPFL, 2010. DOI : 10.5075/epfl-thesis-4830.
A. Fossati; M. Dimitrijevic; V. Lepetit; P. Fua : From Canonical Poses to 3D Motion Capture Using a Single Camera; Ieee Transactions On Pattern Analysis And Machine Intelligence. 2010. DOI : 10.1109/TPAMI.2009.108.
D. Weinland; M. Özuysal; P. Fua : Making Action Recognition Robust to Occlusions and Viewpoint Changes. 2010. European Conference on Computer Vision, Heraklion, Greece, September, 2010.
E. Türetken; C. Blum; G. Gonzalez Serrano; P. Fua : Reconstructing Geometrically Consistent Tree Structures from Noisy Images. 2010. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Beijing, China, September 20-24, 2010. p. 291-299.
A. Lucchi; K. Smith; R. Achanta; V. Lepetit; P. Fua : A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images. 2010. International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, September 20-24, 2010.
K. Konolige; J. Bowman; J. Chen; P. Mihelich; M. Calonder et al. : View-Based Maps; International Journal of Robotics Research. 2010. DOI : 10.1177/0278364910370376.
M. Calonder; V. Lepetit; C. Strecha; P. Fua : BRIEF: Binary Robust Independent Elementary Features. 2010. 11th European Conference on Computer Vision (ECCV). p. 778-792.
M. Özuysal / P. Fua; V. Lepetit (Dir.) : Learning Pose Invariant and Covariant Classifiers from Image Sequences. Lausanne, EPFL, 2010. DOI : 10.5075/epfl-thesis-4746.
A. Shaji; A. Varol; L. Torresani; P. Fua : Simultaneous Point Matching and 3D Deformable Surface Reconstruction. 2010. 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, U.S.A., June 13-19, 2010.
A. Fossati / P. Fua (Dir.) : Bridging the Gap between Detection and Tracking for 3D Human Motion Recovery. Lausanne, EPFL, 2010. DOI : 10.5075/epfl-thesis-4699.
M. Calonder; V. Lepetit; P. Fua : Pareto-optimal Dictionaries for Signatures. 2010. Conference on Computer Vision and Pattern Recognition, June.
M. Özuysal; M. Calonder; V. Lepetit; P. Fua : Fast Keypoint Recognition using Random Ferns; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. DOI : 10.1109/TPAMI.2009.23.
J. Berclaz / P. Fua; F. Fleuret (Dir.) : Pedestrian localization, tracking and behavior analysis from multiple cameras. Lausanne, EPFL, 2010. DOI : 10.5075/epfl-thesis-4629.
E. Tola; V. Lepetit; P. Fua : Daisy: An Efficient Dense Descriptor Applied to Wide Baseline Stereo; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. DOI : 10.1109/TPAMI.2009.77.
F. Moreno-Noguer; M. Salzmann; V. Lepetit; P. Fua : Capturing 3D Stretchable Surfaces from Single Images in Closed Form. 2009. Computer Vision and Pattern Recognition (CVPR).
C. Strecha; A. Lindner; K. Ali; P. Fua : Training for Task Specific Keypoint Detection. 2009. DAGM Symposium on Pattern Recognition, Jena, GERMANY, Sep 09-11, 2009. p. 151-160.
M. Calonder; V. Lepetit; P. Fua; K. Konolige; J. Bowman et al. : Compact Signatures for High-speed Interest Point Description and Matching. 2009. IEEE International Conference on Computer Vision, Kyoto, JAPAN, Sep 29-Oct 02, 2009. p. 357-364.
V. Lepetit; F. Moreno-Noguer; P. Fua : EPnP: An Accurate O(n) Solution to the PnP Problem; International Journal Of Computer Vision. 2009. DOI : 10.1007/s11263-008-0152-6.
A. Fossati; M. Salzmann; P. Fua : Observable Subspaces for 3D Human Motion Recovery. 2009. IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA, June 22 - 24, 2009.
M. Calonder; V. Lepetit; P. Fua; K. Konolige; J. Bowman et al. : Compact Signatures for High-speed Interest Point Description and Matching. 2009. International Conference on Computer Vision, September, 2009.
K. Konolige; J. Bowman; J. Chen; P. Mihelich; M. Calonder et al. : View-Based Maps. 2009. Robotics: Science and Systems Conference, Seattle, June 2009.
M. Özuysal; V. Lepetit; P. Fua : Pose Estimation for Category Specific Multiview Object Localization. 2009. Conference on Computer Vision and Pattern Recognition, Miami, 2009. p. 778-785. DOI : 10.1109/CVPR.2009.5206633.
J. Berclaz; A. Shahrokni; F. Fleuret; J. Ferryman; P. Fua : Evaluation of Probabilistic Occupancy Map People Detection for Surveillance Systems. 2009. Proceedings of the IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Miami, Florida, USA, June 20-25, 2009.
K. Ali; F. Fleuret; D. Hasler; P. Fua : Joint Pose Estimator and Feature Learning for Object Detection. 2009. IEEE International Conference on Computer Vision. p. 1373-1380.
A. Shahrokni; T. Drummond; F. Fleuret; P. Fua : Classification-based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation; IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009.
G. Gonzalez; F. Fleuret; P. Fua : Learning Rotational Features for Filament Detection. 2009. International Conference on Computer Vision and Pattern Recognition. p. 1582-1589.
J. Berclaz; F. Fleuret; P. Fua : Multiple Object Tracking using Flow Linear Programming. 2009.
J. Berclaz; F. Fleuret; P. Fua : Multiple Object Tracking using Flow Linear Programming. 2009. 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (Winter-PETS 2009), Snowbird, Utah.
A. Varol; M. Salzmann; E. Tola; P. Fua : Template-Free Monocular Reconstruction of Deformable Surfaces. 2009. International Conference on Computer Vision, Kyoto, September 27 - October 4, 2009.
P. Lagger / P. Fua (Dir.) : Specularities as an information source in the presence of texture. Lausanne, EPFL, 2009. DOI : 10.5075/epfl-thesis-4387.
M. Salzmann / P. Fua (Dir.) : Learning and recovering 3D surface deformations. Lausanne, EPFL, 2009. DOI : 10.5075/epfl-thesis-4270.
P. Lagger; P. Fua : Retrieving multiple light sources in the presence of specular reflections and texture; Computer Vision And Image Understanding. 2008. DOI : 10.1016/j.cviu.2007.11.002.
M. Salzmann; R. Urtasun; P. Fua : Local deformation models for monocular 3D shape recovery. 2008. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, Jun 23-28, 2008. p. 1213-1220.
P. Lagger; M. Salzmann; V. Lepetit; P. Fua : 3D pose refinement from reflections. 2008. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, Jun 23-28, 2008. p. 1205-1212.
M. Calonder; V. Lepetit; P. Fua : Keypoint Signatures for Fast Learning and Recognition. 2008. European Conference on Computer Vision , Marseille, FRANCE, Oct 12-18, 2008. p. 58-71.
F. Moreno-Noguer; V. Lepetit; P. Fua : Pose Priors for Simultaneously Solving Alignment and Correspondence. 2008. European Conference on Computer Vision, Marseille, France, Oct 12-18, 2008. p. 405-418.
M. Salzmann; F. Moreno-Noguer; V. Lepetit; P. Fua : Closed-Form Solution to Non-rigid 3D Surface Registration. 2008. European Conference on Computer Vision , Marseille, FRANCE, Oct 12-18, 2008. p. 581-594.
J. Pilet; C. Strecha; P. Fua : Making Background Subtraction Robust to Sudden Illumination Changes. 2008. European Conference on Computer Vision, Marseille, France, Oct 12-18, 2008. p. 567-580.
G. Gonzalez; F. Fleuret; P. Fua : Automated Delineation of Dendritic Networks in Noisy Image Stacks. 2008. European Conference on Computer Vision.
J. Berclaz; F. Fleuret; P. Fua : Principled Detection-by-classification from Multiple Views. 2008. proceedings of the International Conference on Computer Vision Theory and Applications.
J. Berclaz; F. Fleuret; P. Fua : Multi-Camera Tracking and Atypical Motion Detection with Behavioral Maps. 2008. Proceedings of the 10th European Conference on Computer Vision, Marseille, October 12-18, 2008.
A. Fossati; E. Arnaud; R. Horaud; P. Fua : Tracking Articulated Bodies using Generalized Expectation Maximization. 2008. CVPR Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, Anchorage, June 27-28, 2006.
A. Fossati; P. Fua : Linking Pose and Motion. 2008. European Conference on Computer Vision, Marseille, October 12-18, 2008.
G. Gonzalez; F. Fleuret; P. Fua : Automated Delineation of Dendritic Networks in Noisy Image Stacks. 2008. European Conference on Computer Vision, Marseille, October 13-16, 2008. p. 214-227.
M. Calonder; V. Lepetit; P. Fua : Keypoint Signatures for Fast Learning and Recognition. 2008. European Conference on Computer Vision, Marseilles, October 2008.
C. Strecha; L. Van Gool; P. Fua : IA Generative Model for True Orthorectification. 2008. ISPRS Congress, Beijing, 2008. p. 303-308.
C. Strecha; L. Van Gool; P. Fua : A Generative Model for True Orthorectification. 2008. ISPRS Congress Beijing 2008, Beijing, 2008.
J. Berclaz; F. Fleuret; P. Fua : Principled Detection-by-Classification from Multiple Views. 2008. International Conference on Computer Vision Theory and Applications, Funchal, Madeira, January 22-25, 2008. p. 375-382.
E. Tola; V. Lepetit; P. Fua : A fast local descriptor for dense matching. 2008. Conference on Computer Vision and Pattern Recognition, Alaska, USA, June 24-26, 2008.
J. Pilet / P. Fua (Dir.) : Augmented reality for non-rigid surfaces. Lausanne, EPFL, 2008. DOI : 10.5075/epfl-thesis-4192.
F. Moreno-Noguer; V. Lepetit; P. Fua : Accurate non-iterative O(n) solution to the PnP problem. 2007. 11th IEEE International Conference on Computer Vision, Rio de Janeiro, BRAZIL, Oct 14-21, 2007. p. 2252-2259.

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§ 93-1 Adoption of standards.
§ 93-2 Local enforcing agency.
§ 93-3 Duties of agency.
§ 93-5 Appointment and qualifications of Chief Fire Marshal and employees.
§ 93-8 Smoke detectors and carbon monoxide alarms required; certification; fee.
§ 93-9 Permit fee; Fire Code status certificate fee.
§ 93-10 Fire Advisory Board.
§ 93-11 Board of Appeals.
§ 93-12 Enforcement; violations and penalties.
§ 93-15 Placement and contents.
§ 93-16 Maintenance and inspection.
§ 93-20 Violations and penalties.
Volunteer Fire Department — See Ch. 11.
Fire insurance — See Ch. 89.
Housing standards — See Ch. 112.
Smoke detectors — See Ch. 163.
Pursuant to Section 11 of the Uniform Fire Safety Act (P.L. 1983, c. 383), the New Jersey Uniform Fire Code (N.J.A.C. 5:70-1 et seq.) shall be enforced locally in the Township of Deerfield.
Editor's Note: See N.J.S.A. 52:27D-192, et seq.
The local enforcing agency shall be the Township of Deerfield through its Bureau of Fire Prevention, which is hereby created therein. The Bureau of Fire Prevention shall hereinafter be known as the "local enforcing agency."
The local enforcing agency shall enforce the Uniform Fire Code in all buildings, structures and premises within the established boundaries of the Township of Deerfield other than one- and two-unit owner-occupied dwellings used exclusively for dwelling purposes and buildings, structures and premises owned or operated by the federal government, interstate agencies or the state.
The Bureau of Fire Prevention established by § 93-2 of this article shall be under the direct supervision and control of the Chief Fire Marshal, who shall report to the Township Committee.
The Bureau of Fire Prevention shall have at least one paid inspector, who may be the Chief Fire Marshal.
The Bureau of Fire Prevention, under the direct supervision of the Chief Fire Marshal, shall have a backup fire inspector with the title of Deputy Chief Fire Marshal.
Appointment and qualifications of the Chief Fire Marshal. The Chief Fire Marshal shall be certified by the state and appointed by the Township Committee from a list of three names submitted to the Township Committee by the Township Fire Chiefs.
Appointment and qualifications of inspectors and other employees. Inspectors and other employees of the enforcing agency shall be appointed by the governing body upon the recommendation of the Chief Fire Marshal. All life-hazard use inspectors shall be certified by the state.
The Chief Fire Marshal shall serve for a term of one year, running from January 1 to December 31. The Deputy Chief Fire Marshal shall serve for a term of one year, running from January 1 to December 31. Any vacancy shall be filled for the unexpired term.
Removal from office. The Chief Fire Marshal, inspectors and other employees of the agency shall be subject to removal by the Township Committee for inefficiency or misconduct. Before removal from office, all persons shall be afforded an opportunity to be heard by the Township Committee or a hearing officer designated by the same.
The Bureau of Fire Prevention established by § 93-2 of this article shall carry out periodic inspections of life-hazard uses required by the Uniform Fire Code on behalf of the Commissioner of the New Jersey Department of Community Affairs.
Uses not classified above that are subject to the Uniform Fire Code will be classified as business uses.
Uses required to register with the state as life-hazard uses shall not be required to register under this section.
At the discretion of the Chief Fire Marshal, vacant buildings will be charged and inspected according to the previous use of the building.
In accordance with N.J.A.C. 5:70-4.19 of the Uniform Fire Code, all one- and two-family dwellings shall be certified to have had installed approved and tested smoke detectors and carbon monoxide alarms at the time of sale of the property.
It shall be the responsibility of the owner or his agent to obtain the proper certification of smoke detector and carbon monoxide alarm installation before the sale is final.
An employee of a licensed title company.
A licensed New Jersey real estate agent.
A licensed New Jersey fire subcode official.
A certified New Jersey Chief Fire Marshal.
A certified New Jersey fire inspector.
A certificate will be issued after an inspection or certification of compliance has been completed.
Requests for a CSDCMAC (certificate of smoke detector and carbon monoxide alarm compliance) received more than 10 business days prior to the change of occupant: $35.
Requests for a CSDCMAC received four to 10 business days prior to the change of occupant: $70.
Requests for a CSDCMAC received fewer than four business days prior to the change of occupant: $125.
The application fees for permits listed in N.J.A.C. 5:70-2.9(c) shall be established in accordance with the Uniform Fire Code of the State of New Jersey.
The cost for the issuance of a certificate of Fire Code status shall be $25.
The Fire Advisory Board shall consist of the following members: the Chief Fire Marshal, a Township Committee Member (Public Safety Chairman), the Coordinator of Emergency Management, the Township Fire Chiefs and the Assistant Chiefs.
The Board shall meet on a monthly basis.
The Board shall communicate and discuss firesafety, training and operational concerns among its members.
The Board shall assist the Chief Fire Marshal in formulating operational policies consistent with his responsibilities under the Uniform Fire Code.
The Board shall be chaired by the Chief Fire Marshal, who shall call the meetings.
Pursuant to N.J.A.C. 5:70-2.19(a)(1) of the Uniform Fire Code, any person aggrieved by any action of the local enforcing agency shall have the right to appeal to the local Construction Board of Appeals. If no such body exists, appeals shall be made to the County Construction Board of Appeals.
Enforcement, violations and penalties shall be managed in conformity with the Uniform Fire Safety Act, the Uniform Fire Code and all other laws of the State of New Jersey.
Any life hazard buildings with an automatic fire alarm or sprinkler system.
Any building or business that has any type of security gate(s) or gated communities.
Any building that contains large quantities of hazardous materials.
Any building that has a history of chronic false or nuisance fire alarms.
Prior to purchase and installation, the building or business owner shall meet with a representative from the Fire Marshal's office to determine the size box, placement and contents in said Knox Box.
It shall be the responsibility of the building or business owner to inform the Fire Marshal's office any time a key needs to be added to or changed from the Knox Box.
It shall be the responsibility of the business owner to ensure the maintenance of the Knox Box. Each Box will be checked annually by a representative of the Fire Marshal's office. Any repairs shall be noted at the time and repairs shall be completed within the specified time frame.
It shall be the duty of the Fire Marshal's office to enforce this article. The Knox Box installation shall be sited by the Fire Marshal's office during any inspection or complaint of any building or business. If the business or property owner refuses to comply, a citation will be issued through the Deerfield Township Municipal Court.
Should a business or building owner whose building fits the criteria listed above wish to not install a Knox Box System, said owner may submit a letter stating their reasons to the Chief Fire Marshal with in 10 days of being cited to install. At that time, the Chief Fire Marshal shall review the situation with the Fire Chief and a member of the Deerfield Township Committee to determine if a variance is warranted.
Upon notice from the Fire Marshal's office, a building or business owner shall have 30 days to order and receive a Knox Box. Upon receipt of the Knox Box, the building owner shall notify the Fire Marshal's office and make an appointment determine the placement and contents of the box.
The Knox Box shall be installed and ready for use no later than 10 days after meeting with the Fire Marshal's office.
The building or business owner shall not be responsible for delays caused by either the Fire Marshal's office or the Knox Box vendor.
Refusal to comply with this article shall be in violation of this article and punished by a penalty of no less than $100 and not more than $500 plus court costs.
If the violation remains unabated after 30 days from the date the original violation is issued a daily penalty of $100 will be imposed for each day thereafter that the violation remains unabated.


These terms and conditions (hereinafter referred to as "business conditions") of a natural person Magdalena Obedient, established Main 130, 25063 Velen, identification number: 88643336, registered in the Trade register of the Municipal part of Prague 4, ref: P4-DP/4471/12/BEN/1085861/5 (hereinafter referred to as "the seller") govern in accordance with the provisions of § 1751 paragraph. 1 of act no. 89/2012 Coll., the the civil code (hereinafter the "civil code") mutual rights and obligations of the parties arising in connection with or on the basis of the purchase contract (hereinafter "purchase contract") concluded between the seller and another natural person (hereinafter referred to as "the buyer") via internet shop of the seller. Online store is operated by the seller on a web page at the internet address www.mp-patchwork.cz (hereinafter referred to as "website"), and through the interface of the website (hereinafter referred to as "web interface of the shop").
1.1. Terms and conditions do not apply to caseswhere a person who intends to purchase goods from the seller is a legal entity or person who is ordering goods within their business or in its separate profession.
1.2. Any provisions derogating from the commercial terms can be negotiated in the purchase contract. Divergent arrangements in the purchase contract take precedence over the provisions of the business terms and conditions.
1.3. The provisions of the business terms and conditions are an integral part of the purchase contract. Terms and conditions are written in Czech language. The purchase contract can be concluded in Czech, English or German language.
1.4. The text of the business conditions seller may modify or supplement. This provision shall not affect the rights and obligations established during the effectiveness of the previous version of business conditions.
2.1. On the basis of the buyer's registration made on the website the buyer can access their user interface. From its user interface buyer can order goods (hereinafter "user account"). In the event that the web interface allows shop, the purchaser may also order goods without registration directly from the web interface of the shop.
2.2. When registering on the website and when ordering goods the buyer is obliged to state correctly and truthfully all the data. The data referred to in the user account, the buyer when any change required to update. The data referred to by the buyer in the user account and when ordering goods are considered correct by the seller.
2.3. Access to the user account is secured by username and password. The buyer is obliged to maintain confidentiality regarding information necessary to access his user account.
2.4. The buyer is not entitled to allow the use of user's account to third parties.
2.5. The seller may cancel the user account, especially if the buyer your user account for more than 12 consecutive months not used, or if the buyer breaches its obligations under the purchase agreement (including business conditions).
2.6. The buyer acknowledges that user account may not be accessible continuously, in particular with regard to necessary maintenance of hardware and software equipment of the seller, or necessary maintenance of hardware and software equipment of third parties.
3.1. The web interface of the shop contains information about the goods and including the prices of individual goods. Prices of goods are inclusive of value added tax and all related fees. The prices of goods remain in force for a period of time, when they are displayed in the web interface of the shop. This provision is not limited to the seller conclude a purchase contract under individually negotiated conditions.
3.2. The web interface of trade also includes information about the costs associated with packaging and delivery of the goods.
3.2.4. information on the costs associated with the delivery of the goods (hereinafter collectively referred to as "the order").
3.3. Before sending the order to the seller, the buyer is allowed to check and change the data in the order submitted by the buyer, and also with regard to the option buyer to detect and correct errors when entering data into the order. The buyer sends the order by clicking on the button "CONFIRM ORDER". The data referred to in the order are considered correct by the seller.
3.4. Sending the order is considered such act of the buyer, which is an undisputed way identifies the ordered goods, purchase price, the person of the buyer, the method of payment of the purchase price, and is binding upon the contracting parties a draft of the contract. A condition for the validity of the order is filling in all the mandatory data in the order form, familiarity with these terms and conditions on the website and the confirmation of the buyer with these terms and conditions met.
3.5. The seller immediately after receipt of order the buyer confirms receipt by e-mail, and the e-mail address of the buyer specified in the user interface or in the order (hereinafter referred to as "electronic address of the buyer").
3.6. The seller is always entitled, depending on the nature of the order (quantity of goods, amount of the purchase price, estimated shipping costs) to ask the buyer for additional order confirmation (e.g. in writing or by telephone).
3.7. The contractual relationship between the seller and the buyer arises delivery order acceptance (acceptance) that is sent by the seller to the buyer by electronic mail, and the e-mail address of the buyer.
3.8. The buyer acknowledges that the seller is not obliged to conclude a purchase contract , especially with persons who have previously substantially breached the purchase contract (including terms and conditions).
3.9. In the event that any of the requirements referred in the order the seller cannot meet, send to the buyer on the buyer's email address amended offer, indicating the possible variants of the order and shall request the opinion of the buyer.
3.10. The amended offer is considered a new draft of the purchase contract and the purchase contract in such a case is concluded when acceptance of the buyer via electronic mail.
3.11. The buyer agrees with the use of distance communication means when concluding the purchase contract. Costs incurred by the buyer when using means of distance communication in connection with concluding the purchase contract (costs of internet connection, costs of telephone calls) shall be borne by the buyer himself.
For payments in the CZECH republic: no 2000649036/2010 held with the company Fio Banka, and.with.
For payments in EUR : 2100263448 /2010, Fio Bank and.with.
4.2. Together with the purchase price, the buyer is obliged to pay the seller also costs associated with packaging and delivery of the goods at an agreed rate. Unless expressly stated otherwise, the further purchase price and the costs associated with the delivery of the goods.
4.3. In the case of payment on delivery, the purchase price shall be payable upon receipt of goods. In the case of cashless payment, the purchase price is payable within 5 days from conclusion of the purchase contract.
4.4. In the case of cashless payment, the buyer is obliged to reimburse the purchase price of the goods together with stating the variable symbol of the payment. In the case of cashless payment, the buyer's obligation to pay the purchase price is fulfilled at the moment of crediting the relevant amount to the account of the seller.
4.5. The seller is entitled, in particular in the event that the buyer does not receive additional confirmation of the order (art. 3.6), require payment of the full purchase price before sending the goods to the buyer.
4.6. Any discounts from the price of the goods supplied by the seller to the buyer cannot be mutually combined.
4.7. If it is in the usual course of trade or if so stipulated by generally binding legal regulations, issue the seller regarding payments made under a contract to the buyer an invoice. The seller is not a payer of value added tax. Invoice issued by the seller to the buyer after payment of the price of goods and send it electronically to the electronic address of the buyer.
5.1. The buyer acknowledges that pursuant to § 1837 of the civil code, cannot be, inter alia, to withdraw from the purchase contract: for the supply of goods, which was adjusted according to the wishes of the buyer or to his person (fleece, yardage, reinforcements), for the supply of audio or video recordings or computer program, if violated their original packaging, for the delivery of newspapers, periodicals or magazines, and manuals.
5.2. If this is not the case referred to in the article. 5.1 or about other cases where you cannot withdraw from the purchase agreement, the purchaser has, in accordance with the provisions of § 1829 paragraph. 1 of the civil code the right to withdraw from the purchase agreement within fourteen (14) days from receipt of the goods, and in the case that the subject of the purchase of several types of goods or delivery of several parts, the period runs from the date of receipt of the last delivery of the goods. Withdrawal from the purchase contract must be sent to the seller within the deadline referred to in the previous sentence.
5.4. In case of withdrawal from the purchase contract according to the article. 5.2 of the business terms of the purchase agreement from the outset. The goods must be returned to the seller within fourteen (14) days from the withdrawal from the contract to the seller. If the purchaser withdraws from the purchase contract, the buyer shall bear the costs associated with returning the goods to the seller, and even in that case, when the goods cannot be returned because of their nature usual postal route. The goods must be returned to the seller undamaged and unworn off state and, if possible, in the original packaging.
5.5. In the case of withdrawal from the contract according to the article. 5.2 commercial terms, the seller returns the funds received from the buyer within fourteen (14) days from the withdrawal from the purchase contract by the buyer, and it in the same way the seller from the buyer. The seller is also entitled to return performance provided by the buyer when returning the goods by the buyer or in another way, if the buyer will agree and will not incur the buyer additional costs. If the purchaser withdraws from the purchase contract, the seller is not obliged to return received funds to the buyer before the buyer returns the goods or proves that the goods the seller sent.
5.6. The buyer acknowledges that if the goods returned by the buyer is damaged, worn or partially consumed, the seller becomes entitled to damages incurred by him. Be entitled to reimbursement of the damage incurred to the goods, the seller is entitled to unilaterally against the buyer's right to refund the purchase price.
5.7. By the time of receipt of the goods by the buyer the seller shall be entitled at any time withdraw from the purchase agreement. In such a case, the seller returns the purchase price to the buyer without undue delay, and direct transfer to an account designated by the buyer.
5.8. If together with the goods provided the buyer a gift is a gift agreement between the seller and the buyer concluded with a condition subsequent that if there is a withdrawal from the contract buyer loses donation agreement regarding such a gift of efficiency and the buyer shall together with the goods to the seller return provided gift.
6.1. The method of delivery of goods determined by the seller, unless the purchase contract provides otherwise.In the case that it is way of transport is negotiated on the basis of a specific request of the buyer, the buyer bears the risk and any additional costs associated with this way of transport.
6.2. If the seller under the purchase contract must deliver the goods to the place specified by the buyer in the order, the buyer is obliged to accept the goods upon delivery.
6.3. In the event that due to reasons on the side of the buyer of the goods to be delivered repeatedly or otherwise than specified in the order, the buyer is obliged to pay the costs associated with repeated delivery of goods, resp. costs associated with another delivery method.
6.4. When taking the goods from the carrier, the buyer is obliged to check the integrity of the packaging of the goods and in case of any defects immediately notify the carrier. In the case of finding a breach of the package indicative of intrusion into the consignment buyer may not take the package. By signing the delivery note buyer confirms that the shipment of goods meet all the terms and conditions and any later claims of infringement of container shipments can not be disregarded.
7.1. The rights and obligations of the contracting parties regarding the rights of defective performance shall be governed by the relevant generally binding regulations (particularly the provisions of § 1914 to 1925, § 2099 to 2117 and sections 2161 to 2174 of the civil code).
7.2. The seller is responsible to the buyer that the thing sold is in conformity with the purchase contract, especially that it is without defects. Accordance with the purchase contract means that the thing sold has quality and properties required by the agreement, by the seller, manufacturer or his representative discussed, or on the basis of the carried out by them advertising the expected, or the quality and properties for the cause of such kind of the usual, that meets the requirements of the legislation, it is in corresponding quantity, measure or weight and corresponds to the purpose which the seller for the use of the case stated or for that matter usually used.
7.3. In the event that, on receipt by the buyer is not in conformity with the purchase contract (hereinafter referred to as "contradiction with the purchase contract"), buyer has the right to the seller free of charge and without undue delay, to put into the state corresponding purchase contract, and according to the request of the buyer in exchange either case, or its repair; if such a procedure possible, the buyer may request a reasonable discount from the price or withdraw from the contract. This does not apply if the buyer about the conflict with the contract knew or conflict with the contract he has caused. The contradiction with the purchase contract, which takes effect within six (6) months from the date of receipt of the goods, is considered a contradiction existing in its takeover, if not in the nature of things or if it is proven otherwise.
7.4. The provisions referred to in the article. 7.3 these terms and conditions shall not apply to goods sold at a lower price to a defect for which the lower price was negotiated..
7.5. Rights of defective performance shall be exercised by the buyer at the seller at the address of the business. For the moment the complaint is considered to be the moment when the seller receives the claimed goods from the buyer.
7.6. Other rights and obligations of the parties relating to the liability of the seller for defects can modify the complaints procedure seller.
8.2. The seller is not in relation to the buyer bound by any codes of conduct within the meaning of § 1826 paragraph. 1. e) of the civil code.
8.4. The seller is entitled to sell the goods on the basis of a trade license. Trade control is carried out in the framework of its competence, the competent trade licensing office. Supervision of the protection of personal data performed by the Office for the protection of personal data. The Czech trade inspection performs within the defined scope, inter alia, supervision over compliance with act no. 634/1992 Coll., on consumer protection, as amended.
8.5. The buyer hereby assumes the risk of change of circumstances within the meaning of § 1765, paragraph. 2 of the civil code.
9.1. The protection of personal data of the buyer, who is a natural person, is provided by act no. 101/2000 Coll., on the protection of personal data, as amended.
9.2. The buyer agrees with processing these personal data: name and surname, address of residence, identification number, tax identification number, electronic mail address, telephone number and date of birth (hereinafter collectively referred to as "personal information").
9.3. The buyer agrees to the processing of personal data by the seller, for the purpose of realization of the rights and obligations of the purchase contract and for the purposes of the management of the user account. If the buyer chooses another option, agrees to the processing of personal data by the seller also for the purpose of sending information and business communication from the buyer. Consent to the processing of personal data in its entirety under this article is not a condition which itself would prevent the conclusion of the purchase contract.
9.4. The buyer acknowledges that it is obligated to your personal data (when registering, in his user account, when ordering from the web interface by trade) correctly and truthfully and that it shall without undue delay inform the seller about change in their personal data.
9.5. Processing of personal data by the buyer the seller may appoint a third party as a processor. In addition to the persons transporting the goods will not be personal data by the seller without the prior consent of the buyer passed on to third parties.
9.6. Personal data will be processed for an indefinite period of time. Personal data will be processed electronically in an automated manner or in printed form non-automated manner.
9.7. The buyer confirms that the provided personal data are accurate and that he was advised that this is a voluntary provision of personal data.
9.8.2. require the seller or processor to remedy the situation.
9.9. If the buyer asks for information about the processing of their personal data, the seller is obliged to deliver this information. The seller has the right to provide information pursuant to the previous sentence, require reasonable compensation not exceeding the costs necessary for the provision of information.
10.1. The buyer agrees to receive information related to goods, services or company the seller to the buyer's email address and agrees to receive commercial communications seller to buyer's email address.
10.2. The buyer agrees with storing cookies on his computer. In the event that the purchase is on a web page can be made and the obligations of the seller under the purchase agreement to perform, without causing store cookies on your computer buyer, the buyer may consent under the previous sentence revoked at any time.
11.1. Notification concerning relations of the seller and the buyer,in particular regarding the withdrawal from the purchase contract, must be received by mail by registered letter, unless the purchase contract provides otherwise. The notice shall be delivered to the relevant contact address of the other party, and shall be deemed delivered and effective upon their delivery through the mail, with the exception of the notification of withdrawal from the contract by the buyer, when is the resignation effective, if notification by the buyer within the time limit for the withdrawal is sent.
11.2. In the inbox is considered also a notice, whose takeover was the addressee refused, which was not picked up in the storage time, or that are returned as undeliverable.
11.3. The contracting parties can normal correspondence to each other to deliver via electronic mail, and the e-mail address indicated in the user account of the buyer or specified by the buyer in the purchase order or to the address listed on the web page of the seller.
12.1. If the relationship of the purchase agreement includes an international (foreign) element, then the parties agree that the relationship is governed by Czech law. This does not affect the consumer rights resulting from generally binding legal regulations.
12.2. If any provision of these terms and conditions is invalid or ineffective, or such happens, the invalid provision a provision whose meaning is invalid provision as close as possible. The invalidity or unenforceability of one provision shall not affect the validity of the other provisions. Changes and supplements to the purchase contract or business conditions require written form.
12.3. The purchase agreement including the gtc is archived by the seller in electronic form and is not accessible.
12.4. The annex to the business conditions form the model form for withdrawal from the purchase contract.
12.5. Contact information seller: address for service Magdalena Obedient, Main 130, 25063 Velen, e-mail address info@mp-patchwork.cz, phone +420 736 616 295 .