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检索条件"机构=Computer Vision and Pattern"

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Fuzzy logic based selera recognition

Fuzzy logic based selera recognition

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Abhijit Das Umapada Pal Miguel Angel Ferrer Ballester Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Islas Canarias ES

In this paper a sclera recognition and validation system is proposed. Here sclera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments.

关键词： Image segmentation Iris recognition Level set Histograms Training Feature extraction

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Dynamic hand gesture early recognition based on Hidden Semi-Markov Models

Dynamic hand gesture early recognition based on Hidden Semi-...

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IEEE International Conference on Robotics and Biomimetics

作者： Qianqian Wang Yuanrong Xu Yen-Lun Chen Yong Wang Xinyu Wu University of Science and Technology of China Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong. Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences

ISBN: (纸本)9781479973989

Real-time performance can be greatly improved, if the early recognition is implemented. In this paper, a dynamic hand gesture early recognition system is proposed. The system can recognize the gesture before it is completed. Our method is based on the Hidden Semi-Markov Models. Three-dimensional information of the gesture trajectory collected by leapmotion is the main data we used. Experiments on the dataset which we established demonstrate the effectiveness of our method.

关键词： Hidden Markov models Gesture recognition Training Conferences Trajectory Arrays

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A new efficient and adaptive sclera recognition system

A new efficient and adaptive sclera recognition system

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IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.

关键词： Iris recognition Image segmentation Histograms Training Feature extraction Adaptive systems

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A Simple Context Dependent Suggestion System 22

A Simple Context Dependent Suggestion System

引用

22nd Text REtrieval Conference, TREC 2013

作者： Roy, Dwaipayan Bandyopadhyay, Ayan Mitra, Mandar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Contextual Suggestion Problem focuses on search techniques for complex information needs that are highly dependent on context and user interest. In this paper, we present our approach to providing user and context dependent suggestions. The official evaluation scores for our submitted run are reported and compared to the overall median scores (across all 34 runs). © 2013 22nd Text REtrieval Conference, TREC 2013 - Proceedings. All Rights Reserved.

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Semi-supervised multi-feature learning for person re-identification

Semi-supervised multi-feature learning for person re-identif...

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2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

作者： Figueira, Dario Bazzani, Loris Minh, Ha Quang Cristani, Marco Bernardino, Alexandre Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy ISR Instituto Superior Técnico Lisboa Portugal

ISBN: (纸本)9781479907038

Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary state-of-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem. © 2013 IEEE.

关键词： Cameras

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Statistical analysis of visual attentional patterns for video surveillance 1

引用

18th Iberoamerican Congress on pattern Recognition, CIARP 2013

作者： Roffo, Giorgio Cristani, Marco Pollick, Frank Segalin, Cristina Murino, Vittorio Department of Computer Science University of Verona Italy Pattern Analysis and Computer Vision Dept. Istituto Italiano di Tecnologia Italy School of Psychology University of Glasgow United Kingdom

ISBN: (数字)9783642418273

ISBN: (纸本)9783642418266

We show that the way people observe video sequences, other than what they observe, is important for the understanding and the prediction of human activities. In this study, we consider 36 surveillance videos, organized in four categories (confront, nothing, fight, play ): the videos are observed by 19 people, ten of them are experienced operators and the other nine are novices, and the gaze trajectories of both populations are recorded by an eye tracking device. Due to the proved superior ability of experienced operators in predicting violence in surveillance footage, our aim is to distinguish the two classes of people, highlighting in which respect expert operators differ from novices. Extracting spatio-temporal features from the eye tracking data, and training standard machine learning classifiers, we are able to discriminate the two groups of subjects with an average accuracy of 80.26%. The idea is that expert operators are more focused on few regions of the scene, sampling them with high frequency and low predictability. This can be thought as a first step toward the advanced automated analysis of video surveillance footage, where machines imitate as best as possible the attentive mechanisms of humans. © Springer-Verlag 2013.

关键词： Eye tracking

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Multi-script off-line Signature verification: A two stage approach 2

Multi-script off-line Signature verification: A two stage ap...

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2nd International Workshop on Automated Forensic Handwriting Analysis, AFHA 2013

作者： Pal, Srikanta Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Signature identification and verification are of great importance in authentication systems. The purpose of this paper is to introduce an experimental contribution in the direction of multi-script off-line signature identification and verification using a novel technique involving off-line English, Hindi (Devnagari) and Bangla (Bengali) signatures. In the first evaluation stage of the proposed signature verification technique, the performance of a multi-script off-line signature verification system, considering a joint dataset of English, Hindi and Bangla signatures, was investigated. In the second stage of experimentation, multi-script signatures were identified based on the script type, and subsequently the verification task was explored separately for English, Hindi and Bangla signatures based on the identified script result. The gradient and chain code features were employed, and Support Vector Machines (SVMs) along with the Modified Quadratic Discriminate Function (MQDF) were considered in this scheme. From the experimental results achieved, it is noted that the verification accuracy obtained in the second stage of experiments (where a signature script identification method was introduced) is better than the verification accuracy produced following the first stage of experiments. Experimental results indicated that an average error rate of 20.80% and 16.40% were obtained for two different types of verification experiments.

关键词： Support vector machines

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Learning Flexible Binary Code for Linear Projection Based Hashing with Random Forest

Learning Flexible Binary Code for Linear Projection Based Ha...

引用

International Conference on pattern Recognition

作者： Shuze Du Wei Zhang Shifeng Chen Yafei Wen Chengdu Institute of Computer Applications Chinese Academy of Sciences Sichuan China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Media Lab. Huawei Technologies Co. Ltd. China Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Guangdong China The Chinese University of Hong Kong Hong Kong University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479952106

Existing linear projection based hashing methods have witnessed many progresses in finding the approximate nearest neighbor(s) of a given query. They perform well when using a short code. But their code length depends on the original data dimension, thus their performance can not be further improved with higher number of bits for low dimensional data. In addition, in the case of high dimensional data, it is not a good choice to produce each bit by a sign function. In this paper, we propose a novel random forest based approach to cope with the above shortcomings. The bits are obtained by recording the paths when a point traversing each tree in the forest. Then we propose a new metric to calculate the similarity between any two codes. Experimental results on two large benchmark datasets show that our approach outperforms its counterparts and demonstrate its superiority over the existing state-of-the-art hashing methods for descriptor retrieval.

关键词： Vegetation Vectors Binary codes Encoding Computational modeling Radio frequency Principal component analysis

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Bag-of-features HMMs for segmentation-free Bangla word spotting 13

Bag-of-features HMMs for segmentation-free Bangla word spott...

引用

4th International Workshop on Multilingual OCR, MOCR 2013

作者： Rothacker, L. Fink, G.A. Banerjee, P. Bhattacharya, U. Chaudhuri, B.B. Department of Computer Science TU Dortmund University 44221 Dortmund Germany Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-700 0108 India

ISBN: (纸本)9781450321143

In this paper we present how Bag-of-Features Hidden Markov Models can be applied to printed Bangla word spotting. These statistical models allow for an easy adaption to different problem domains. This is possible due to the integration of automatically estimated visual appearance features and Hidden Markov Models for spatial sequential modeling. In our evaluation we are able to report high retrieval scores on a new printed Bangla dataset. Furthermore, we outperform state-of-the-art results on the well-known George Washington word spotting benchmark. Both results have been achieved using an almost identical parametric method configuration. © 2013 ACM.

关键词： Hidden Markov models

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On road vehicle make and model recognition via sparse feature coding

On road vehicle make and model recognition via sparse featur...

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Iranian Machine vision and Image Processing (MVIP)

作者： A. Nazemi M. J. Shafiee Z. Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Automatic vehicle Make and Model Recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit (OMP), some variation of Sparse Coding (SC) methods and compares them to choose the best one. Our method employs the sparse feature coding methods on dense Scale-Invariant Feature Transform (SIFT) features and Support Vector Machine (SVM) for classification. The proposed system is examined by an Iranian on road vehicles dataset, which its samples are in different point of views, various weather conditions and illuminations.

关键词： Feature extraction Vehicles Encoding Dictionaries Image coding Kernel computer vision

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