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Nonnegative Matrix Factorization with Gibbs Random Field modeling

Nonnegative Matrix Factorization with Gibbs Random Field mod...

引用

International conference on computer vision workshops (ICCV workshops)

作者： Shengcai Liao Zhen Lei Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we present a Gibbs Random Field (GRF) modeling based Nonnegative Matrix Factorization (NMF) algorithm, called GRF-NMF. We propose to treat the component matrix of NMF as a Gibbs random field. Since each component presents a localized object part, as usually expected, we propose an energy function with the prior knowledge of smoothness and locality. This way of directly modeling on the structure of components makes the algorithm able to learn sparse, smooth, and localized object parts. Furthermore, we find that at each update iteration, the constrained term can be processed conveniently via local filtering on components. Finally we give a well established convergence proof for the derived algorithm. Experimental results on both synthesized and real image databases shows that the proposed GRF-NMF algorithm significantly outperforms other NMF related algorithms in sparsity, smoothness, and locality of the learned components.

关键词： Matrix decomposition Sparse matrices Convergence Spectral analysis Clustering algorithms Bayesian methods conferences Biometrics National security Laboratories

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Multilinear Isometric Embedding for visual pattern analysis

Multilinear Isometric Embedding for visual pattern analysis

引用

International conference on computer vision workshops (ICCV workshops)

作者： Yan Liu Yang Liu Keith C.C. Chan Department of Computing Hong Kong Polytechnic University Hong Kong China

This paper proposes a novel tensor based dimensionality reduction algorithm called Multilinear Isometric Embedding (MIE) based on a representative manifold learning algorithm Isomap. Unlike Isomap that unfolds input data to the vector form, MIE directly works on more general tensor representation and utilizes iterative strategy to seek the low-dimensional equivalence, which best preserves the global geometry. By avoiding the problems caused by data vectorization, MIE reduces the data analysis difficulty and computational cost. More importantly, MIE keeps the intrinsic tensor structure of the data in low-dimensional representation. Meanwhile, MIE inherits the merits of Isomap, i.e., the ability of uncovering the global geometry of high-dimensional observations. By providing explicit embedding function, MIE makes the embedding of new data points to the low-dimensional space straightforward. Experiments on various datasets validate the effectiveness of proposed method.

关键词： pattern analysis Tensile stress Geometry Principal component analysis Linear discriminant analysis Computational efficiency Laplace equations computer vision Independent component analysis Iterative algorithms

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Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance

Real-time vision-based multiple vehicle detection and tracki...

引用

2009 ieee International conference on Systems, Man and Cybernetics, SMC 2009

作者： Chen, Yen-Lin Wu, Bing-Fei Fan, Chung-Jui Dept. of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical and Control Engineering National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781424427949

This study presents an effective system for detecting and tracking moving vehicles in nighttime traffic scene for traffic surveillance. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied on the nighttime road-scene images. This automatic multilevel thresholding approach can provide robustness and adaptability for the detection system to be operated well under various illumination conditions at night. The extracted bright objects are processed by a spatial clustering and tracking procedure by locating and analyzing the spatial and temporal features of vehicle light patterns, and then identifying and classifying the moving cars and motorbikes in the traffic scenes. Experimental results demonstrate that the proposed approach is feasible and effective for vehicle detection and identification in various nighttime environments for traffic surveillance. ©2009 ieee.

关键词： Vehicles

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The Algorithm for Matching 2D Contours Based on V-system

The Algorithm for Matching 2D Contours Based on V-system

引用

Chinese conference on pattern recognition/1st CJK Joint Workshop on pattern recognition

作者： Zhang, Caixia Miao, Zhihua Sun, Fengmei N China Univ Technol Inst Image Proc & Pattern Recognit Beijing 100144 Peoples R China

ISBN: (纸本)9781424441990

Curve matching is one of key issues in computer vision, image analysis and pattern recognition. Based on discrete V-transform, the distance is calculated between curves using the descriptor of V-system to find the matching curves, and then the matching parameters are evaluated in this article. The new approach can find efficiently the rough location of a short extracted image curve in a long reference curve. Different from the existing approaches, it needn't to extract feature points. Extensive tests show that it is efficient.

关键词： V-system Curve matching Similar transformation

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A novel method of feature points identification using multi-scale technology

A novel method of feature points identification using multi-...

引用

Chinese conference on pattern recognition/1st CJK Joint Workshop on pattern recognition

作者： Gai, Shaoyan Da, Feipeng Yang, Weiguang Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Nanjing 210096 Peoples R China

ISBN: (纸本)9781424441990

High precision identification of feature points is an important technology and one of the bases of computer vision, image analysis and image processing. In the practical applications, the feature points can not be identified easily in various conditions of light illumination, visual angle, texture, and perspective projection. And in many cases, the size of feature point is small, the context is uncertain and the shape of feature point is different from each other. According to the properties of actual vision images, a novel method of feature point identification using multi-scale detecting technology is presented. In this thesis, an automatic circle feature point detection system is developed based on digital image processing techniques, in which, the feature points are detected in two scales. In large-scale recognition, the image pre-processing is carried out to reduce the impact of image noise, and the feature points with various shape deformation can be found out. And then, the founded points are checked along with small scale detecting. The relationship of the points and their mappings in three dimensional space is obtained and used to judge if the point is a real feature point one by one. Thus the deformed feature points in various perspective projection conditions can be recognized correctly. Experimental results show that this approach for feature points identification is not only fast but also robust. The proposed technology is a kind of useful method for contour extraction and feature points identification in the fields of computer vision and image analysis.

关键词： pattern recognition feature point image segmentation shape description

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Efficient retrieval of deformable shape classes using local self-similarities

Efficient retrieval of deformable shape classes using local ...

引用

International conference on computer vision workshops (ICCV workshops)

作者： Ken Chatfield James Philbin Andrew Zisserman Department of Engineering Science University of Oxford UK

We present an efficient object retrieval system based on the identification of abstract deformable `shape' classes using the self-similarity descriptor of Shechtman and Irani. Given a user-specified query object, we retrieve other images which share a common `shape' even if their appearance differs greatly in terms of colour, texture, edges and other common photometric properties. In order to use the self-similarity descriptor for efficient retrieval we make three contributions: (i) we sparsify the descriptor points by locating discriminative regions within each image, thus reducing the computational expense of shape matching; (ii) we extend to enable matching despite changes in scale; and (iii) we show that vector quantizing the descriptor does not inhibit performance, thus providing the basis of a large-scale shape-based retrieval system using a bag-of-visual-words approach. Performance is demonstrated on the challenging ETHZ deformable shape dataset and a full episode from the television series Lost, and is shown to be superior to appearance-based approaches for matching non-rigid shape classes.

关键词： Shape Image retrieval Deformable models Photometry Large-scale systems TV Heart pattern matching Information retrieval Cows

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Line of Sight Stabilization Technology Based on Visual Attitude Estimation

Line of Sight Stabilization Technology Based on Visual Attit...

引用

Chinese conference on pattern recognition/1st CJK Joint Workshop on pattern recognition

作者： Chen, Yi Bo, Yuming Lv, Ming Nanjing Univ Sci & Technol Coll Automat Nanjing 210094 Peoples R China Nanjing North Informat Ind Grp Nanjing 211153 Peoples R China

ISBN: (纸本)9781424441990

Referring to the possible problem of gyro random drift effect on the Line of Sight(LOS) stabilization accuracy, a method for the LOS stabilization based on visual attitude estimation is proposed. A monocular vision system is established by four coplanar reference points on the ground and a double-focus CCD camera in the carrier. The quaternion is used to represent the transform relation between geodetic coordinate system and carrier coordinate system, and the square-root unscented Kalman Filter in which the current statistical model is applied to obtain the system state equations is used to estimate the attitude parameters. On this basis, the velocity interference model of the LOS is derived according to coordinate transformation and rigid body kinematics, and the method of feedforward control is adapted to isolate the influence of the LOS velocity disturbance. The simulation results indicate that the proposed method is effective in stabilizing the LOS without changing the structure of the tracking units.

关键词： Attitude estimation computer vision square-root UKF LOS stabilization feedforward control

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Reduced training of convolutional neural networks for pedestrian detection

Reduced training of convolutional neural networks for pedest...

引用

6th International conference on Information Technology and Applications, ICITA 2009

作者： Nguyen, Giang Hoang Phung, Son Lam Bouzerdoum, Abdesselam School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong NSW 2522 Australia

ISBN: (纸本)9789810830298

Pedestrian detection is a vision task with many practical applications in video surveillance, road safety, autonomous driving and military. However, it is much more difficult compared to the detection of other visual objects, because of the tremendous variations in the inner region as well as the outer shape of the pedestrian pattern. In this paper, we propose a pedestrian detection approach that uses convolutional neural network (CNN) to differentiate pedestrian and non-pedestrian patterns. Among several advantages, the CNN integrates feature extraction and classification into one single, fully adaptive structure. It can extract two-dimensional features at increasing scales, and it is relatively tolerant to geometric, local distortions in the image. Although the CNN has good generalization performance, training CNN classifier is time-comsuming. Therefore, we present an efficient training approach for CNN. Through the experiments, we show that it is possible to design networks in a fraction of time taken by the standard learning approach. © ICITA 2009.

关键词： Object detection

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Fine-Grained Tracking of Human Mobility in Dense Scenarios

Fine-Grained Tracking of Human Mobility in Dense Scenarios

引用

ieee Annual Communications society Sensor, Mesh and Ad Hoc Communications and Networks SECON workshops

作者： Sabrina Gaito Elena Pagani Gian Paolo Rossi Computer Science Dept. Universita degli Studi di Milano Milano Italy

This paper envisions an urban scenario where people carry radio devices that can be dynamically networked, by exploiting human contact opportunities, to create unplanned, improvised and localized wireless connectivity, which has been recently called pocket switched networks (PSN).The paper focuses on the radio device (pocket mobility trace recorder, or PMTR) we have on purposely designed and developed to improve this understanding by enabling the gathering of rich and detailed mobility data sets from experiments in real mobility settings. The main contribution of the paper is twofold: we firstly describe the architecture of the radio devices and, secondly, we provide some evidence of the impact short contacts have on forwarding in dense settings.

关键词： Humans Energy consumption Batteries Disruption tolerant networking Employment Bandwidth computer science Protocols pattern analysis Delay

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Fuzzy inference based non-daily behavior pattern detection for elderly people monitoring system

Fuzzy inference based non-daily behavior pattern detection f...

引用

Annual International conference of the ieee Engineering in Medicine and Biology society (EMBC)

作者： Hirokazu Seki Department of Electrical Electronics and Computer Engineering Chiba Institute of Technology Chiba Japan

This paper describes an abnormal behavior detection system based on an omni-directional vision sensor as one of the important elements in realizing rdquoSensing and Robotic Support Roomrdquo for elderly people. Such support rooms are expected to be further developed in the future with the high performance to automatically recognize elderly people's actions and behavior patterns and detect the unusual patterns using some sensors and to support their daily motions using some robotic manipulator control systems. The proposed monitoring system using an omni-directional vision sensor automatically learns the daily behavior patterns and detects the unusual behavior patterns and actions using fuzzy inference approach. The fuzzy rules are constructed using image feature values such as the area and center-of gravity values extracted from the captured image sequence and their patterns appearance frequency. Unusual behavior patterns can be automatically detected based on the possibility distribution inference method. Some experiments based on the investigation of elderly people's typical daily behavior patterns show the effectiveness of the proposed system.

关键词： Fuzzy systems Senior citizens Monitoring Sensor systems Robot sensing systems Robotics and automation Sensor phenomena and characterization Robot vision systems pattern recognition Motion detection

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