Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches.
In more recent papers authors pay more attention on developing solutions which can be done automatically with less user guidance. @cite_8 finds regions with high difference between x and y derivatives, @cite_13 calculates oriented histograms to find patches with dominant direction, @cite_7 relies on morphology operations to detect both 1D and 2D barcodes, reporting high accuracy on their own data. The work of S " o r " o s @cite_1 is notable as they compare their own algorithm with other works mentioned in this paragraph. They demonstrate that their approach is superior on the same dataset WWU Muenster Barcode Database (Muenster). Their algorithm is based on the idea that 1D barcodes have many edges, 2D barcodes have many corners, while text areas have both many edges and many corners.
@cite_1 @cite_13 @cite_7 @cite_8
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Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method. With the proliferation of built-in cameras barcode scanning on smartphones has become widespread in both consumer and enterprise domains. To avoid making the user precisely align the barcode at a dedicated position and angle in the camera image, barcode localization algorithms are necessary that quickly scan the image for possible barcode locations and pass those to the actual barcode decoder. In this paper, we present a barcode localization approach that is orientation, scale, and symbology (1D and 2D) invariant and shows better blur invariance than existing approaches while it operates in real time on a smartphone. Previous approaches focused on selected aspects such as orientation invariance and speed for 1D codes or scale invariance for 2D codes. Our combined method relies on the structure matrix and the saturation from the HSV color system. The comparison with three other real-time barcode localization algorithms shows that our approach outperforms the state of the art with respect to symbology and blur invariance at the expense of a reduced speed. This paper shows new algorithms and the implementations of image reorganization for EAN QR barcodes in mobile phones. The mobile phone system used here consists of a camera, mobile application processor, digital signal processor (DSP), and display device, and the source image is captured by the embedded camera device. The introduced algorithm is based on the code area found by four corners detection for 2D barcode and spiral scanning for 1D barcode using the embedded DSP. This algorithm is robust for practical situations and the DSP has good enough performance for the real-time recognition of the codes. The performance of our image processing is 66.7 frames sec for EAN code and 14.1 frames sec for QR code image processing, and this is sufficient performance for practical use. The released mobile phone had performance of 5-10 frames sec including OS and subsystem overheads. With the ubiquitous of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. However, the previous works for extracting 2D barcodes from an image do not consider the distortion resulted from camera shake. Moreover, the previous works for extracting 2D barcodes from an image do not take a complex background into account. In this paper, therefore, we propose an efficient and effective algorithm to extract 2D barcode from a complex background in a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition. A 2D color barcode can hold much more information than a binary barcode. Barcodes are often intended for consumer use where using a cellphone, a consumer can take an image of a barcode on a product, and retrieve relevant information about the product. The barcode must be read using computer vision techniques. While a color barcode can hold more information, it makes this vision task in consumer scenarios unusually challenging. We present our approach to the localization and segmentation of a 2D color barcode in such challenging scenarios, along with its evaluation on a diverse collection of images of Microsoft's recently launched high capacity color barcode (HCCB). We exploit the unique trait of barcode reading: the barcode decoder can give the vision algorithm feedback, and develop a progressive strategy to achieve both - high accuracy in diverse scenarios as well as computational efficiency. Barcode technology is essential in automatic identification, and is used in a wide range of real-time applications. Different code types and applications impose special problems, so there is a continuous need for solutions with improved performance. Several methods exist for code localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need reliable automatic barcode localization, e.g. conveyor belts and automated production, where missed detections cause loss of profit. Our goal is to detect automatically, rapidly and accurately the barcode location with the help of extracted image features. We propose a new algorithm variant, that outperforms in both accuracy and efficiency other detectors found in the literature using similar ideas, and also improves on the detection performance in detecting 2D codes compared to our previous algorithm. The linear 1D barcode is the main tagging system for billions of products sold each day. Barcodes have many advantages but require a laser scanner for fast and robust scanning. Solutions exist to read barcodes from cell phones but they assume a carefully framed image within the field of view. This undermines the true potential of barcodes in a wide range of scenarios. In this paper we present a real time technique to detect barcodes in the wild from video streams. Our technique outperforms the state-of-the-art passive techniques both in accuracy and speed. Potential commercial applications enabled by such passive scanning system are also discussed in this paper. In this paper we present an algorithm for the recognition of 1D barcodes using camera phones, which is highly robust regarding the the typical image distortions. We have created a database of barcode images, which covers typical distortions, such as inhomogeneous illumination, reflections, or blurriness due to camera movement. We present results from experiments with over 1,000 images from this database using a Matlab implementation of our algorithm, as well as experiments on the go, where a Symbian C++ implementation running on a camera phone is used to recognize barcodes in daily life situations. The proposed algorithm shows a close to 100 accuracy in real life situations and yields a very good resolution dependent performance on our database, ranging from 90.5 (640 × 480) up to 99.2 (2592 × 1944). The database is freely available for other researchers. There is increased interest in the use of color barcodes to encode more information per area unit than regular, black-and-white barcodes. For example, Microsoft's HCCB technology uses 4 or 8 colors per patch. Unfortunately, the observed color of a surface depends as much on the illuminant spectrum (and other viewing parameters) as on the surface reflectivity, which complicates the task of decoding the content of the barcode. A popular solution is to append to the barcode a “palette” with the reference colors. In this paper, we propose a new approach to color barcode decoding, one that does not require a reference color palette. Our algorithm decodes groups of color bars at once, exploiting the fact that joint color changes can be represented by a low-dimensional space. Decoding a group of bars (a “barcode element”) is thus equivalent to searching for the nearest subspace in a dataset. We also propose algorithms to select subsets of barcode elements that can be decoded with low error probability. Our experimental results show that our barcode decoding algorithm enables substantial information rate increase with respect to system that display a color palette, at a very low decoding error rate. The finite rate of innovation (FRI) principle is developed for sampling a class of non-bandlimited signals that have a finite number of degrees of freedom per unit of time, i.e., signals with FRI. This sampling scheme is later extended to three classes of sampling kernels with compact support and applied to the step edge reconstruction problem by treating the image row by row. In this paper, we regard step edges as 2D FRI signals and reconstruct them block by block. The step edge parameters are obtained from the 2D moments of a given image block. Experimentally, our technique can reconstruct the edge more precisely and track the Cramer-Rao bounds (CRBs) closely with a signal-to-noise ratio (SNR) larger than 4 dB on synthetic step edge images. Experiments on real images show that our proposed method can reconstruct the step edges under practical conditions, i.e., in the presence of various types of noise and using a real sampling kernel. The results on locating the corners of data matrix barcodes using our method also outperform some state-of-the-art barcode decoders. This paper presents COBRA - a visible light communication (VLC) system for off-the-shelf smartphones. COBRA encodes information into specially designed 2D color barcodes and streams them between screen and camera of smartphones. Due to the directionality and short range of visible light, COBRA can preserve user privacy and security in many near field communication scenarios such as opportunistic data exchange between smartphones. We develop a new 2D color barcode for COBRA that is optimized for streaming between small-size screen and low-speed camera of smartphones. COBRA adapts the size and layout of code blocks in streamed barcodes to deal with the significant image blur in mobile environments, and adopts new image processing techniques to achieve real-time barcode stream decoding. Our approach is evaluated through extensive experiments on Android smartphones. Linear barcodes are the principal labeling system for retail products. Barcode reader apps found on smartphones always assume that the localization and framing of the barcode is performed manually by a sighted human operator. This is problematic for visually-impaired people since they don't know where to position the camera to scan the barcode. To solve this problem we propose a hand-free interface to detect barcode using a wearable camera. The user rotate a query product in front of him her and is informed when and where the barcode is visible. The challenge is to detect small barcodes at arm's length in a video with potentially large motion blur. In this paper we propose a novel technique for barcode detection using very little computation (adapted to wearable systems), presenting very good robustness to blur and size variations, and able to run on HD video streams. The proposed system perform significantly better than the state-of-the-art methods on existing public datasets, while being much faster. A new and challenging egocentric product video dataset is also provided with this paper. The use of colors increases the information storage capacities in barcodes. Increasing the number of colors to encode information makes the decoding a challenging task due to the dependency of the surface color on the illuminant spectrum, viewing parameters, printing device and material, color fading in addition to other nuisance parameters. A popular solution is the use of a color palette of reference colors printed with barcode. The decoding becomes more challenging if a mobile phone as a decoding device is used due to the capture of images from different distances as well as angles. In addition, the barcode images are often blurry because of incorrect focus or camera shake. We present an iterative decoding algorithm that decodes the colors of all barcode patches across the barcode by minimizing the overall observation error. Our method is able to decode colors in presence of blur using a small number of colors yet ensuring high information density. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 × 480, JPEG compressed) in an average time of 400-500 ms. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences. We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes. This improvement is based on a new temporal signature: motion barcode for lines. Motion barcode is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar, so the search for corresponding epipolar lines can be limited only to lines having similar barcodes. The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry. We describe a new smartphone app called BLaDE (Barcode Localization and Decoding Engine), designed to enable a blind or visually impaired user find and read product barcodes. Developed at The Smith-Kettlewell Eye Research Institute, the BLaDE Android app has been released as open source software, which can be used for free or modified for commercial or non-commercial use. Unlike popular commercial smartphone apps, BLaDE provides real-time audio feedback to help visually impaired users locate a barcode, which is a prerequisite to being able to read it. We describe experiments performed with five blind visually impaired volunteer participants demonstrating that BLaDE is usable and that the audio feedback is key to its usability. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Computing the epipolar geometry between cameras with very different viewpoints is often very difficult. The appearance of objects can vary greatly, and it is difficult to find corresponding feature points. Prior methods searched for corresponding epipolar lines using points on the convex hull of the silhouette of a single moving object. These methods fail when the scene includes multiple moving objects. This paper extends previous work to scenes having multiple moving objects by using the “Motion Barcodes”, a temporal signature of lines. Corresponding epipolar lines have similar motion barcodes, and candidate pairs of corresponding epipoar lines are found by the similarity of their motion barcodes. As in previous methods we assume that cameras are relatively stationary and that moving objects have already been extracted using background subtraction.