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Correlation filter cascade for facial landmark localization

Correlation filter cascade for facial landmark localization

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IEEE Workshop on Applications of computer vision (WACV)

作者： Hamed Kiani Galoogahi Terence Sim Pattern Analysis and Computer Vision Department Istituto Italiano di Tecnologia Genova Italy School of Computing National University of Singapore Singapore

ISBN: (纸本)9781509006427

The application of correlation filters for the task of facial landmark detection has been studied by many vision works. Their success, however, is limited by the presence of large pose variations, expression and occlusion in face images. Moreover, existing correlation filters may suffer from poor discrimination to distinguish visually similar landmarks such as the right and left eyes. In this work, we present a new framework, referred to as Correlation Filter Cascade, to address the above limitations. The proposed framework consists of a set of correlation filters with different spatial supports (sizes) which are connected together in a cascade form. More specifically, the size of filters decreases from the lower to upper levels. Filters at lower levels implicitly code face shape information since they are trained using large patches stemmed from face images. This avoids ambiguous detections caused by landmarks with similar appearance. Detections in these levels, however, may not be accurate and suffer from small localization errors, mainly caused by face pose, expression and occlusion. Therefore, locations detected by lower levels will be further used by the higher levels to narrow down their search regions. Since the filters at higher levels have smaller size, they are less affected by pose, expression and occlusion, and thus, can perform more accurately. The evaluation on BioID and LFPW shows the superiority of our method compared to prior correlation filters and leading facial landmark detectors.

关键词： Face Correlation Training Shape Robustness Feature extraction Testing

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International Conference on pattern Recognition

作者： Anjan Dutta Umapada Pal Josep Lladós Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science Dept. Universitat Autònoma de Barcelona Bellaterra Spain

ISBN: (纸本)9781509048489

This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300.

关键词： Feature extraction Reservoirs Histograms Visualization Forgery

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PERSON RE-IDENTIFICATION USING SPARSE REPRESENTATION WITH MANIFOLD CONSTRAINTS

PERSON RE-IDENTIFICATION USING SPARSE REPRESENTATION WITH MA...

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IEEE International Conference on Image Processing

作者： Behzad Mirmahboub Hamed Kiani Amran Bhuiyan Alessandro Perina Baochang Zhang Alessio Del Bue Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Italian Institute of Technology Genova Italy School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9781467399623

Human re-identification is still a challenging task due to the human pose and illumination variations. Nowadays, surveillance cameras with high frame rate are capable of capturing several consecutive frames from each person. Multi-shot images provide richer information of the target person compared to a single-shot image. They, however, produce a high cost of information redundancy which may degrade the performance of re-identification systems. In this paper, we propose a novel framework that combines sparse coding and manifold constraints to extract discriminative information from multi-shot images of one pedestrian for person re-identification across a set of non-overlapped surveillance cameras. The evaluation over two standard multi-shot datasets shows very competitive accuracy of our framework against the state-of-the-art.

关键词： Manifolds Cameras Training Dictionaries Brightness Lighting Redundancy

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An Automatic Off-Line Short Answer Assessment System Using Novel Hybrid Features

An Automatic Off-Line Short Answer Assessment System Using N...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Hemmaphan Suwanwiwat Umapada Pal Michael Blumenstein College of Business James Cook University Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India School of Software University of Technology Sydney Australia

To date, paper-based examinations are still in use worldwide on all levels of education levels (e.g. secondary, tertiary levels). However, literature regarding off-line automatic assessment systems employing off-line handwriting recognition is not numerous. This paper proposes an off-line automatic assessment system employing a hybrid feature extraction technique - a newly proposed Modified Direction and Gaussian Grid Feature (MDGGF), along with its enhanced technique. In this study other original feature extraction techniques, together with their enhanced features, were also used for feature extraction technique efficiency comparison purposes. Classifiers, namely artificial neural networks and support vector machines, were selected to be employed in the experiments. Two types of datasets were employed in the experiment for both feature extraction technique accuracy and efficiency comparisons. The best correctly recognised rate of 98.33% with 100% accuracy was obtained when employing the proposed MDGGF to the off-line automatic assessment system.

关键词： Feature extraction Gravity Training Testing Artificial neural networks Support vector machines Handwriting recognition

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Offline handwritten Devanagari word recognition: Information fusion at feature and classifier levels 3

Offline handwritten Devanagari word recognition: Information...

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3rd IAPR Asian Conference on pattern Recognition, ACPR 2015

作者： Shaw, Bikash Bhattacharya, Ujjwal Parui, Swapan Kumar Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9781479961009

This article presents our recent study on fusion of information at feature and classifier output levels for improved performance of offline handwritten Devanagari word recognition. We consider here two state-of-the-art features, viz., Directional Distance Distribution (DDD) and Gradient-Structural-Concavity (GSC) features along with multi-class SVM classifiers. Here, we study various combinations of DDD features along with one or more features from the GSC feature set. We experiment by presenting different combined feature vectors as input to SVM classifiers. Also, the output vectors of different SVM classifiers fed with different feature vectors are combined by another SVM classifier. The combination of the outputs of two SVMs each being fed with a different feature vector provides superior performance to the performance of a single SVM classifier fed with the combined feature vector. Experimental results are obtained on a large handwritten Devanagari word sample image database of 100 Indian town names. The recognition results on its test samples show that SVM recognition output of DDD features combined with the SVM output of GSC features improves the final recognition accuracy significantly. © 2015 IEEE.

关键词： Classification (of information)

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Writer Identification from offline isolated Bangla characters and numerals 13

Writer Identification from offline isolated Bangla character...

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13th International Conference on Document Analysis and Recognition, ICDAR 2015

作者： Adak, Chandranath Chaudhuri, Bidyut B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9781479918058

Writer identification is an essential component in computational forensic. In this paper, we attempt to do this job based only on isolated characters and numerals. For that, at first, some points of interest (keypoints) on the image are detected by structural analysis and SIFT based detector. Then we calculate a set of features within a certain neighborhood of the keypoint and employ fusion rule on multiple probabilistic SVM classifiers output for writer identification. For experimental analysis, a database containing 212,300 isolated Bangla orthosyllabic characters and numerals are generated with the help of 100 writers. We obtain fairly good result to identify a writer. We also try to find a small set of highly discriminative characters storing extra information about the writing style of an individual. © 2015 IEEE.

关键词： handwriting recognition natural language processing probability support vector machines Bangla orthosyllabic characters SIFT based detector computational forensic isolated Bangla characters isolated Bangla numerals isolated characters multiple probabilistic SVM classifiers structural analysis writer identification Chlorine Discriminative Bangla character Handwriting analysis Writer identification Numerals handwriting recognition structural analysis Structural analysis Support Vector Network Natural Language Processing Writers Chlorine

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Exemplary Sequence Cardinality: An effective application for word spotting 13

Exemplary Sequence Cardinality: An effective application for...

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13th International Conference on Document Analysis and Recognition, ICDAR 2015

作者： Mondal, Tanmoy Ragot, Nicolas Ramel, Jean-Yves Pal, Umapada France Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781479918058

In this paper proposed. ESC combines several abilities of other sequence matching algorithms e.g. DTW, SSDTW, CDP, FSM, MVM, OSB1. Depending on the application domain, ESC can be tuned to∗∗a new sequence matching algorithm called as Exemplary Sequence Cardinality (ESC) is behave such as these different sequence matching algorithms. Its generality and robustness comes from its ability to find subsequences (as in CDP and SSDTW), to skip outliers inside the target sequences (as in MVM and FSM) and also in the query sequence (as in OSB ) and it has the ability to have many to one and one to many correspondences (as in DTW) between the elements of the query and the target sequences. It's special characteristic of skipping noisy elements from query sequence along with other afore mentioned properties gives it an edge over FSM. In case of word spotting application, the outliers skipping capability of ESC makes it less sensible to local variations in the spelling of words, and also to noise present in the query and/or in the target word images. Due to it's capability of sub-sequence matching, the ESC algorithm has the ability to retrieve a query inside a line or piece of line. Finally, its multiple matching facilities (many to one and one to many matching) is proven to be well advantageous in case of different length of target and query sequences due to the variability in scale, font, type/size factors. By experimenting on printed historical document images, we have demonstrated the interest of proposed ESC algorithm in specific cases when incorrect word segmentation and word level local variations occur regularly. © 2015 IEEE.

关键词： Dynamic programming

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Performance evaluation of DTW and its variants for word spotting in degraded documents 13

Performance evaluation of DTW and its variants for word spot...

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13th International Conference on Document Analysis and Recognition, ICDAR 2015

作者： Mondal, Tanmoy Ragot, Nicolas Ramel, Jean-Yves Pal, Umapada Tours France Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781479918058

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results. © 2015 IEEE.

关键词： Dynamic programming

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Clustered semi-supervised relevance feedback 15

Clustered semi-supervised relevance feedback

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Ghosh, Kripabandhu Parui, Swapan Kumar Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

ISBN: (纸本)9781450337946

In relevance feedback, first-round search results are used to boost second-round search results. Two forms have been traditionally considered: exhaustively labelled feedback, where all first-round results to depth k arc annotated for relevance by the user;and blind feedback, where the top-k results arc all assumed to be relevant. In this paper, we consider an intermediate, semi-supervised scheme, in which only a subset of results is selected for annotation, and then their labels are propagated to their nearest neighbours. Specifically, we use clustering to determine the nearest-neighbour groups, and seed selection to choose documents for annotation. We find that the effectiveness of this method is indistinguishable from the exhaustive relevance feedback, and is significantly higher than both blind feedback and the use of the annotated subset alone. We show that this approach works well in environments in which some but limited amounts of human feedback arc available, such as early case assessment in e-discovcry. © 2015 ACM.

关键词： relevance assessment cluster hypothesis e-discovery

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An Investigation of Novel Combined Features for a Handwritten Short Answer Assessment System

An Investigation of Novel Combined Features for a Handwritte...

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International Workshop on Frontiers in Handwriting Recognition

作者： Hemmaphan Suwanwiwat Umapada Pal Michael Blumenstein College of Business Law and Governance (Information Technology) James Cook University Australia Computer Vision and Pattern Recognition Unit Indian Institute India School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509009824

This paper proposes an off-line automatic assessment system utilising novel combined feature extraction techniques. The proposed feature extraction techniques are 1) the proposed Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (WRL_MDGGF), 2) the proposed Gravity, Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (G_WRL_MDGGF). The proposed feature extraction techniques together with their original features and other combined feature extraction techniques were employed in an investigation of the efficiency of feature extraction techniques on an automatic off-line short answer assessment system. The proposed system utilised two classifiers namely, artificial neural networks and Support Vector Machines (SVMs), two type of datasets and two different thresholds in this investigation. Promising recognition rates of 94.85% and 94.88% were obtained when the proposed WRL_MDGGF and G_WRL_MDGGF were employed, respectively, using SVMs.

关键词： Feature extraction Gravity Training Reservoirs Testing Support vector machines

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