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检索条件"机构=Computer Vision and Pattern"

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Piecewise single view Photometric Stereo with multi-view constraints

Piecewise single view Photometric Stereo with multi-view con...

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IEEE International Conference on Image Processing

作者： Reza Sabzevari Alessio Del Bue Vittorio Murino PAVIS - Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia (IIT) Genova Italy

This paper presents a novel Photometric Stereo approach for static views that recasts the problem into a piecewise formulation. The proposed algorithm, called Piecewise Photometric Stereo (PPS), entails several advantages in respect to previous global approaches. It is intrinsically more efficient, since reconstructing the surface in patches is computationally faster than reconstructing the global surface. Each patch has been associated an individual photometric model rather than a single global model as used in classical approaches. In this way, the piecewise formulation may grasp more complex lighting effects. Finally, the global metric properties of the shape is preserved using the multi-view constraints. In this pipeline, structure from motion is exploited to define such set of constraints and to compose a 3D mesh representing the metric structure of the object. Real results with ground truth show the positive performance of our algorithm compared with a classical global approach for Photometric Stereo.

关键词： Surface reconstruction Shape Lighting Image reconstruction Stereo vision Measurement Pipelines

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A multiple kernel learning approach to multi-modal pedestrian classification

A multiple kernel learning approach to multi-modal pedestria...

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International Conference on pattern Recognition

作者： Marco San-Biagio Aydın Ulaş Marco Crocco Marco Cristani Umberto Castellani Vittorio Murino Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9781467322164

Pedestrian detection is a key problem in many computer vision applications, especially in surveillance and security systems. To this end, information integration from different imaging modalities, such as thermal infrared and visible spectrum, can significantly improve the detection rate in respect to mono-modal strategies. For this reason, an effective fusion scheme is necessary to combine the information presented by multiple sensors. In this paper, we propose a pedestrian classification method based on the multiple kernel learning framework; standard pixel features (such as spatial derivatives) from both imaging modalities are employed to learn several feature-related basic kernels and a compound kernel is found as an optimized linear combination of basic kernels. Finally the compound kernel is used to train an SVM. Experiments performed on the OTCBVS dataset [1], demonstrate that our recipe definitely outclasses a wide set of literature fusion modalities.

关键词： Kernel Feature extraction Vectors Training Imaging Support vector machines Standards

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Clustered subsampling for clinically informed diagnostic brain mapping

Clustered subsampling for clinically informed diagnostic bra...

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15th International Conference on Information Fusion, FUSION 2012

作者： Björnsdotter, Malin Sona, Diego Rosenthal, Sonny Dauwels, Justin School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore Pattern Analysis and Computer Vision Fondazione Istituto Italiano di Tecnologia Italy Wee Kim Wee School of Communication and Information Nanyang Technological University Singapore Singapore

ISBN: (纸本)9780982443859

Brain based diagnostic systems have recently received attention as a tool in the characterization and diagnosis of a variety of neurodevelopmental and psychiatric disorders. Nonetheless, a majority of disorders are still diagnosed entirely based on symptom assessments and behavioral correlates. We therefore propose a method for fusing brain responses with clinical measures for improved diagnosis. To this end, we utilized the flexibility of clustered random subspace brain mapping to detect regions where brain responses in conjunction with a clinical measure could reliably differentiate patients from control subjects. We demonstrate the approach on realistically simulated functional magnetic resonance imaging (fMRI) brain activity and a clinical parameter. We show that the method efficiently identifies brain regions where fused analysis of brain responses and clinical parameters improves diagnosis compared to either measure alone. The proposed method is easy to implement and highly flexible, offering an appealing basis for multimodal brain mapping. © 2012 ISIF (Intl Society of Information Fusi).

关键词： Brain mapping

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Off-line English and Chinese Signature Identification Using Foreground and Background Features

Off-line English and Chinese Signature Identification Using ...

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International Joint Conference on Neural Networks

作者： Srikanta Pal Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781467314886

In the field of information security, the usage of biometrics is growing for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is only one of several used to verify the identity of individuals. In this paper, a foreground and background based technique is proposed for identification of scripts from bi-lingual (English/Roman and Chinese) off-line signatures. This system will identify whether a claimed signature belongs to the group of English signatures or Chinese signatures. The identification of signatures based on its script is a major contribution for multi-script signature verification. Two background information extraction techniques are used to produce the background components of the signature images. Gradient-based method was used to extract the features of the foreground as well as background components. Zernike Moment feature was also employed on signature samples. Support Vector Machine (SVM) is used as the classifier for signature identification in the proposed system. A database of 1120 (640 English+480 Chinese) signature samples were used for training and 560 (320 English+240 Chinese) signature samples were used for testing the proposed system. An encouraging identification accuracy of 97.70% was obtained using gradient feature from the experiment.

关键词： Off-line verification systems Signature identification Biometrics Authentication systems SVM

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Recent Advances in Video Based Document Processing: A Review

Recent Advances in Video Based Document Processing: A Review

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Nabin Sharma Umapada Pal Michael Blumenstein Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

Extraction and recognition of text present in video has become a very popular research area in the last decade. Generally, text present in video frames is of different size, orientation, style, etc. with complex backgrounds, noise, low resolution and contrast. These factors make the automatic text extraction and recognition in video frames a challenging task. A large number of techniques have been proposed by various researchers in the recent past to address the problem. This paper presents a review of various state-of-the-art techniques proposed towards different stages (e.g. detection, localization, extraction, etc.) of text information processing in video frames. Looking at the growing popularity and the recent developments in the processing of text in video frames, this review imparts details of current trends and potential directions for further research activities to assist researchers.

关键词： Feature extraction Image edge detection Text recognition Support vector machines Character recognition Accuracy Optical character recognition software

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Stroke Segmentation and Recognition from Bangla Online Handwritten Text

Stroke Segmentation and Recognition from Bangla Online Handw...

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International Workshop on Frontiers in Handwriting Recognition

作者： Nilanjana Bhattacharya Umapada Pal Bose Institute Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper deals with recognition of online handwritten Bangla (Bengali) text. Here, at first, we segment cursive words into strokes. A stroke may represent a character or a part of a character. We selected a set of Bangla words written by different groups of people such that they contain all basic characters, all vowel and consonant modifiers and almost all types of possible joining among them. For segmentation of text into strokes, we discovered some rules analyzing different joining patterns of Bangla characters. Combination of online and offline information was used for segmentation. We achieved correct segmentation rate of 97.89% on the dataset. We manually analyzed different strokes to create a ground truth set of distinct stroke classes for result verification and we obtained 85 stroke classes. Directional features were used in SVM for recognition and we achieved correct stroke recognition rate of 97.68%.

关键词： Support vector machines Handwriting recognition Image segmentation Gold Writing Feature extraction Character recognition

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Joining feature-based and similarity-based pattern description paradigms for object detection

Joining feature-based and similarity-based pattern descripti...

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International Conference on pattern Recognition

作者： Samuele Martelli Marco Cristani Loris Bazzani Diego Tosato Vittorio Murino Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9781467322164

In pattern recognition, two of the main paradigms for describing objects are the feature-based and the (dis)similarity-based one. The former aims at encoding tangible features that characterize the object per-se. The latter gives a relational description of the object, considering the similarities with other reference entities. In this paper, we propose the marriage between these two philosophies: this is possible by considering an object as described by its local parts. Actually, object parts can be described by features, and structural information can be extracted considering the similarities between parts. We cast our intuition in an object detection framework, where we select HOG as feature and simple euclidean distances for the similarity computation. The results show how this hybrid representation outperforms the single paradigms, demonstrating their complementarity.

关键词： Feature extraction computer vision Tensile stress Vectors Benchmark testing Data mining

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Text Independent Writer Identification for Oriya Script

Text Independent Writer Identification for Oriya Script

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Sukalpa Chanda Katrin Franke Umapada Pal Department of Computer Science and Media Technology Gjovik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be ensured. Lack of data threatens system reliability in such cases. We here propose a writer identification system for Oriya script which is capable of performing reasonably well even with small amount of text. Experiments with curvature feature are reported here, using Support Vector Machine (SVM) as classifier. We got promising results of 94.00% writer identification accuracy at first top choice and 99% when considering first three top choices.

关键词： Accuracy Support vector machines Shape Training Handwriting recognition Kernel

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A Comparative Study of Persian/Arabic Handwritten Character Recognition

A Comparative Study of Persian/Arabic Handwritten Character ...

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International Workshop on Frontiers in Handwriting Recognition

作者： Alireza Alaei Umapada Pal P. Nagabhushan Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In recent years, many techniques for the recognition of Persian/Arabic handwritten documents have been proposed by researchers. To test the promises of different features extraction and classification methods and to provide a new benchmark for future research, in this paper a comparative study of Persian/Arabic handwritten character recognition using different feature sets and classifiers is presented. Feature sets used in this study are computed based on gradient, directional chain code, shadow, under-sampled bitmap, intersection/junction/endpoint, and line-fitting information. Support Vector Machines (SVMs), Nearest Neighbour (NN), k-Nearest Neighbour (k-NN) are used as different classifiers. We evaluated the proposed systems on a standard dataset of Persian handwritten characters. Using 36682 samples for training, we tested the proposed recognition systems on other 15338 samples and their detailed results are reported. The best correct recognition of 96.91% is obtained in this comparative study.

关键词： Feature extraction Character recognition Handwriting recognition Shape Support vector machines Accuracy Training

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Effect of perceptual anchorage points on recognition of Bangla characters

Effect of perceptual anchorage points on recognition of Bang...

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1st Indo-Japan Conference on Perception and Machine Intelligence, PerMIn 2012

作者： Bandyopadhyay, Asok Mukherjee, Bhaswati Chaudhuri, Bidyut Baran Centre for Development of Advanced Computing Plot-E2/1 Block GP Salt Lake Kolkata-700091 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata-700108 India

ISBN: (纸本)9783642273865

Character recognition (Printed and Handwritten) system has become an extremely useful tool in Human computer Interaction. Handwriting is a complex perceptual motor task generating linguistic information. Characters reflect shape distinction needed to perceive different phonetic information of words. We have explored 'perceptual processes' of character recognition for developing a cognitive model. Especially, we tried to extract the Perceptual Anchorage Points in the character. An experiment was performed to identify Perceptual anchorage points in a character both for handwritten as well as printed character of different Bangla script fonts including fonts used in Bangladesh. A set of important points and shapes has been found in the experiment. It is noted that deletion of these regions from characters greatly reduces the human cognition of characters. © 2012 Springer-Verlag.

关键词： Character recognition

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