Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods.
Enhancement of low quality images (occurring e.g. in all databases of FVC2004 @cite_5 ) and very low quality prints like latents (e.g. NIST SD27 @cite_13 ) is still a challenge. Techniques based on contextual filtering are widely used for fingerprint image enhancement @cite_29 and a major difficulty lies in an automatic and reliable estimation of the local context, i.e. the local orientation and ridge frequency as input of the GF. Failure to correctly estimate the local context can lead to the creation of artifacts in the enhanced image @cite_41 which consequently tends to increase the number of identification or verification errors.
@cite_41 @cite_5 @cite_29 @cite_13
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In order to ensure that the performance of an automatic fingerprint identification verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy. Automatic feature extraction in latent fingerprints is a challenging problem due to poor quality of most latents, such as unclear ridge structures, overlapped lines and letters, and overlapped fingerprints. We proposed a latent fingerprint enhancement algorithm which requires manually marked region of interest (ROI) and singular points. The core of the proposed enhancement algorithm is a novel orientation field estimation algorithm, which fits orientation field model to coarse orientation field estimated from skeleton outputted by a commercial fingerprint SDK. Experimental results on NIST SD27 latent fingerprint database indicate that by incorporating the proposed enhancement algorithm, the matching accuracy of the commercial matcher was significantly improved. In this paper we propose a semi-automatic approach for the enhancement of very low quality fingerprints such as latent fingerprints. A specific markup tool is designed to allow fingerprint examiners to simply and quickly provide sparse estimates of local orientations and frequencies. These estimates are then interpolated though Delaunay triangulation and fed to a contextual Gabor-based enhancement algorithm that significantly improves the image quality, thus making the successive automatic feature extraction much more reliable. Experimental results (both qualitative and quantitative) confirm the effectiveness of this method over a fully-automatic state-of-the-art approach. Accurate fingerprint recognition presupposes robust feature extraction which is often hampered by noisy input data. We suggest common techniques for both enhancement and minutiae extraction, employing symmetry features. For enhancement, a Laplacian-like image pyramid is used to decompose the original fingerprint into sub-bands corresponding to different spatial scales. In a further step, contextual smoothing is performed on these pyramid levels, where the corresponding filtering directions stem from the frequency-adapted structure tensor (linear symmetry features). For minutiae extraction, parabolic symmetry is added to the local fingerprint model which allows to accurately detect the position and direction of a minutia simultaneously. Our experiments support the view that using the suggested parabolic symmetry features, the extraction of which does not require explicit thinning or other morphological operations, constitute a robust alternative to conventional minutiae extraction. All necessary image processing is done in the spatial domain using 1-D filters only, avoiding block artifacts that reduce the biometric information. We present comparisons to other studies on enhancement in matching tasks employing the open source matcher from NIST, FIS2. Furthermore, we compare the proposed minutiae extraction method with the corresponding method from the NIST package, mindtct. A top five commercial matcher from FVC2006 is used in enhancement quantification as well. The matching error is lowered significantly when plugging in the suggested methods. The FVC2004 fingerprint database, notable for its exceptionally low-quality fingerprints, is used for all experiments. Fingerprint image enhancement is a crucial step in automatic fingerprint recognition. A large number of approaches for filtering the fingerprint image have been suggested. Most of them perform oriented band pass filtering. However, such filters, for example, Gabor filters, may create spurious ridge structure information. This is harmful for the feature extraction and therefore is harmful for the automatic fingerprint recognition. This paper examines the properties of applying the Gabor filter in the fingerprint image enhancement. It shows that the nonsinusoidal-shaped ridge structure, the ridge frequency estimation error, and the small filter size are the causes of creating spurious ridge structures. As a solution, we suggest an adaptive oriented low pass filter instead of the Gabor filter to avoid producing undesired harmful side effects for the automatic fingerprint recognition. In this paper, we describe a technique to automatically enhance the perceptual quality of an image. Unlike previous techniques, where global statistics of the image are used to determine enhancement operation, our method is local and relies on local scene descriptors and context in addition to high-level image statistics. We cast the problem of image enhancement as searching for the best transformation for each pixel in the given image and then discovering the enhanced image using a formulation based on Gaussian Random Fields. The search is done in a coarse-to-fine manner, namely by finding the best candidate images, followed by pixels. Our experiments indicate that such context-based local enhancement is better than global enhancement schemes. A user study using Mechanical Turk shows that the subjects prefer contextual and local enhancements over the ones provided by existing schemes. Biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94 in EER is observed on the FVC2002 DB3 database. An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc. Automated fingerprint identification systems (AFIS) have become a popular tool in many security and law enforcement applications. Most of these systems rely on the matching of fingerprints using the position and orientation of ridge endings and bifurcations within the fingerprint image. While this information is sufficient for matching fingerprints in small databases, it is not discriminatory enough to provide good results on large collections of fingerprint images. This paper presents a means of obtaining additional discriminatory information from fingerprint images by demonstrating a novel method to extract the locations of sweat pores from the grayscale fingerprint image. This is achieved through the implementation of a modified minimum squared error approach. The proposed algorithm is capable of obtaining good results even from images obtained from basic 500 dpi optical live-scan devices despite the common belief that images obtained at this resolution are not of high enough quality. Results of the proposed method are demonstrated on fingerprint images taken both from a common live-scan device and the inked prints of the NIST 4 database. An explanation of the approach is presented, the results are discussed, and future research possibilities are put forth. This paper presents a novel edge orientation adaptive interpolation scheme for resolution enhancement of still images. In order to achieve ideal orientation adaptation, we propose to estimate the local covariance characteristics at low resolution but cleverly use them to direct the interpolation at high resolution based on the resolution invariant property of edge orientation. The orientation adaptive property guarantees the interpolation always go along the edge orientation but not across it. Our new interpolation scheme can generate images with dramatically higher visual quality than linear interpolation techniques while keeping the computational complexity still modest. 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. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. There are fundamental differences in the way fingerprints are compared by forensic examiners and current automatic systems. For example, while automatic systems focus mainly on the quantitative measures of fingerprint minutiae (ridge ending and bifurcation points), forensic examiners often analyze details of intrinsic ridge characteristics and relational information. This process, known as qualitative friction ridge analysis [1], includes examination of ridge shape, pores, dots, incipient ridges, etc. This explains the challenges that current automatic systems face in processing partial fingerprints, mostly seen in latents. The forensics and automatic fingerprint identification systems (AFIS) communities have been active in standardizing the definition of extended feature set, as well as quantifying the relevance and reliability of these features for automatic matching systems. CDEFFS (committee to define an extended feature set) has proposed a working draft on possible definitions and representations of extended features [2]. However, benefits of utilizing these extended features in automatic systems are not yet known. While fingerprint matching technology is quite mature for matching tenprints [3], matching partial fingerprints, especially latents, still needs a lot of improvement. We propose an algorithm to extract two major level 3 feature types, dots and incipients, based on local phase symmetry and demonstrate their effectiveness in partial print matching. Since dots and incipients can be easily encoded by forensic examiners, we believe the results of this research will have benefits to next generation identification (NGI) systems. We propose a new high-quality and efficient single-image upscaling technique that extends existing example-based super-resolution frameworks. In our approach we do not rely on an external example database or use the whole input image as a source for example patches. Instead, we follow a local self-similarity assumption on natural images and extract patches from extremely localized regions in the input image. This allows us to reduce considerably the nearest-patch search time without compromising quality in most images. Tests, that we perform and report, show that the local self-similarity assumption holds better for small scaling factors where there are more example patches of greater relevance. We implement these small scalings using dedicated novel nondyadic filter banks, that we derive based on principles that model the upscaling process. Moreover, the new filters are nearly biorthogonal and hence produce high-resolution images that are highly consistent with the input image without solving implicit back-projection equations. The local and explicit nature of our algorithm makes it simple, efficient, and allows a trivial parallel implementation on a GPU. We demonstrate the new method ability to produce high-quality resolution enhancement, its application to video sequences with no algorithmic modification, and its efficiency to perform real-time enhancement of low-resolution video standard into recent high-definition formats. Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. We propose a simple but effective upsampling method for automatically enhancing the image video resolution, while preserving the essential structural information. The main advantage of our method lies in a feedback-control framework which faithfully recovers the high-resolution image information from the input data, without imposing additional local structure constraints learned from other examples. This makes our method independent of the quality and number of the selected examples, which are issues typical of learning-based algorithms, while producing high-quality results without observable unsightly artifacts. Another advantage is that our method naturally extends to video upsampling, where the temporal coherence is maintained automatically. Finally, our method runs very fast. We demonstrate the effectiveness of our algorithm by experimenting with different image video data.