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Conference on pattern recognition and computer vision

作者： Shen, Minghua Xiao, Huaitie Fu, Qiang Natl Univ Def & Technol Sch Elect Sci & Engn Changsha 410073 Peoples R China

ISBN: (纸本)9780819469526

Support Vector Data Description (SVDD) is a one-class classification method developed in recent years. It has been used in many fields because of its good performance and high executive efficiency when there are only one-class training samples. It has been proven that SVDD has less support vector numbers, less optimization time and faster testing speed than those of two-class classifier such as SVM. At present, researches and acquirable literatures about SVDD multi-class classification are little, which restricts the SVDD application. One SVDD multi-class classification algorithm is proposed in the paper. Based on minimum distance classification rule, the misclassification in multi-class classification is well solved and by applying the threshold strategy the rejection in multi-class classification is greatly alleviated. Finally, by classifying range profiles of three targets, the effect of kernel function parameter and SNR on the proposed algorithm is investigated and the effectiveness of the algorithm is testified by quantities of experiments.

关键词： Support Vector Data Description kernel function parameter pattern recognition multi-class classification

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A novel method for evaluating the validity of the visual attended regions based on SIFT descriptors

A novel method for evaluating the validity of the visual att...

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mippr 2009 - pattern recognition and computer vision: 6th International Symposium on Multispectral Image Processing and pattern recognition

作者： Xiao, Jie Ding, Mingyue Cai, Chao Zhou, Chengping Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478078

Selective visual attention can direct our gaze rapidly towards objects of interest in the view. Better coverage of target region for attention can better serve for recognition. A novel method for evaluating how well the attended regions contribute to the recognition of the target based on SIFT descriptors is proposed in this paper. The method is used to evaluate the attended regions extracted by some visual attention mechanisms on real remote sensor images with different geometric and photometric transformations and for different scene types. The evaluation method proposed in this paper give an explicit, accurate and robust expression about the attended region in visual attention mechanisms and we believe this method could be applicable in visual attention mechanisms in the future work. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Behavioral research

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Camera lens calibration based on virtual model for scanning system

Camera lens calibration based on virtual model for scanning ...

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mippr 2009 - pattern recognition and computer vision: 6th International Symposium on Multispectral Image Processing and pattern recognition

作者： Sun, Xianbin Li, Dehua Jie, Yun Xiong, Caiquan Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Hubei University of Technology Wuhan 430068 China

ISBN: (纸本)9780819478078

Traditional camera lens calibrations need a 3D physical model with many control points on it, the coordination of these points are measured high precision. These points should be evenly distributed in space for calibration precision, and it is difficult to make the model especially big model. In this paper, a calibration plate was made and the plate was moved in its normal direction, parallel, no rotation, it is implemented to provide large area 3D control points with variable Z values, the images of the plate were made in different places. the moving plate is a 3D model, moreover, it could increase control points significant, the plate is much easier to make than traditional 3D physical model, the plate could be big one, the calibration parameters of camera lens were calculated by different control points group, get the mean value of these parameters. This method is proved high precision, stabilization and robustness. Experiments show such an approach is effective for reconstructing 3D objects in computer vision system. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Calibration

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An incremental learning algorithm based on Support Vector Machine for pattern recognition

An incremental learning algorithm based on Support Vector Ma...

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mippr 2009 - pattern recognition and computer vision: 6th International Symposium on Multispectral Image Processing and pattern recognition

作者： Zou, Lamei Zhang, Tianxu Cao, Zhiguo Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478078

With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Classification (of information)

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A genetic feature weighting scheme for pattern recognition

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INTEGRATED computer-AIDED ENGINEERING 2007年第2期14卷 161-171页

作者： Lee, Heesung Kim, Euntai Park, Mignon Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea

This paper proposes a new pattern recognition scheme, combining a new adaptive feature weighting and modified k-Nearest Neighbor (k-NN) rule. The proposed feature weighting method named adaptive-3FW. It uses three non-uniform weight levels (zero weight, middle weight and full weight) to weight each feature. The middle weight value is determined using genetic algorithms (GAs). The proposed adaptive-3FW overcomes overfitting issues and achieves high recognition performance. Novel GA operators tailored for this formulation are introduced to implement the proposed scheme. Further, a modified k-NN is proposed which uses a class-dependent feature weighting strategy. Whilst the conventional pattern recognition systems use the same set of feature weights for all classes, the proposed algorithm uses different sets of feature weights for different classes. Experiments were performed with the UCI repository for machine learning databases and the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

关键词： pattern recognition systems ADAPTIVE control systems FEEDBACK control systems GENETIC programming (computer science) computer vision

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A Fast Randomized Clustering Method Based on a Hypothetical Potential Field

A Fast Randomized Clustering Method Based on a Hypothetical ...

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mippr 2011: pattern recognition and computer vision

作者： Lu, Yonggang Liao, Li Wang, Ruhai School of Information Science and Engineering Lanzhou University Lanzhou Gansu 730000 China Phillip M. Drayer Department of Electrical Engineering Lamar University Beaumont TX 77710 United States

ISBN: (纸本)9780819485786

A novel randomized clustering method is proposed to overcome some of the drawbacks of Mean Shift method. A hypothetical potential field is constructed from all the data points. Different from Mean Shift which moves the kernel window towards high-density region, our method moves the kernel window towards low-potential region. The proposed method is evaluated by comparing with both Mean Shift and K-means++ on three synthetic data sets which represent the clusters of different sizes, different shapes and different distributions. The experiments show that our method can produce more accurate results than both Mean Shift and K-means++. © 2011 SPIE.

关键词： pattern recognition

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vision-based 3D registration of outdoor AR system

Vision-based 3D registration of outdoor AR system

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mippr 2007: pattern recognition and computer vision

作者： Xueling, Wu Qingyun, Du Fu, Ren Tao, Liu School of Resource and Environmental Science Wuhan University 430079 Wuhan China Institute of Geophysics and Geomatics China University of Geosciences 430074 Wuhan China

ISBN: (纸本)9780819469526

One of the key technologies of Outdoor AR is the real-time 3D registration of objects in the real world. The paper puts forward a new hardware registration method which not only borrows the ideas of identification point registration, but improves it to realize tracking registration in video-based outdoor AR, which uses see-through head mounted display (STHMD) loaded on outdoor AR system for showing the result of registration, and employs one color CCD camera capturing video to obtain the world coordinate of scene border. Furthermore, the paper utilizes 3D electronic compass and GPS attached on user's body to calculate transition matrix from the world coordinate system to the camera coordinate system. Then, the transition matrix from the virtual coordinate system to the image plane can be calculated out and 3D virtual object generated by computer model is added into the STHMD as a whole. Synthetically, video-based registration offers a superior approach to 3D registration of dynamic object. Finally, the paper provides the implementation process and designs a test. By the case study, the new method significantly simplifies the registration system and algorithm, and coordination errors are eliminated. The algorithm requires little computation and can be easily realized in real time without delay. Compared with the several existing registration methods, it is significantly improved.

关键词： Real time systems

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Scout: a game speed analysis and tracking system

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MACHINE vision AND APPLICATIONS 2007年第5期18卷 289-299页

作者： Tsai, Ping-Sing Meijome, Todd Austin, Phillip G. Univ Texas Pan Amer Dept Comp Sci Edinburg TX 78541 USA Object Predict Technol LLC Scottsdale AZ 85260 USA

A sporting event game speed analysis and tracking system-Scout is presented in this paper. Common computer vision and pattern recognition methods, such as background subtraction, connected components labelling, morphology filtering, etc., are used for segmenting and tracking moving objects. A new vanishing-points-based method is proposed for the mapping between the screen (image) and physical (field) coordinate systems. The system is designed to evaluate football players' performance and skills from the recorded training camps' footages. The tracking results and performance analysis of real football training camps' video clips are presented.

关键词： SPORTS ARTIFICIAL intelligence computer vision pattern recognition systems IMAGE processing

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Novel framework for producing multi-scale and multi-viewpoint images based on remote sensing stereopair - art. no. 67881G

Novel framework for producing multi-scale and multi-viewpoin...

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Conference on pattern recognition and computer vision

作者： Tan, Ying Cao, Zhiguo Li, Yukun Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

Automatic target recognition(ATR) is the key of the image guidance technology, yet it is difficult to recognize the target by merely depending on the real-time image acquired by flying vehicle cameras, moreover, the task of recognizing the target from the real-time images by the vehicle-carrying image processing system is a hard work itself The main trend of the ATR nowadays is to make utilization of the images produced by high-resolution remote sensing satellite to retrieve the front elevation of the interested region before hand. These front elevations are loaded upon the flying vehicles and are matched with the real-time images acquired by vehicle-carrying cameras to recognize the interested target. Obviously, the key step of this method is to recover the 3D information from 21) images. This paper proposed a framework to produce multi-scale and multi-viewpoint projection images based on remote sensing satellite stereopair by means of photogrammetry and computer vision. First we proposed a algorithm for reconstructing the 3D structure of the target by digital photogrammetric techniques and establishing the 3D model of the target using the OpenGL visualization toolkit. Then the conversion relationship between the world coordinate system and the simulation space coordinate system is provided to produce the front elevation in the simulation space.

关键词： 3D reconstruction multi-scale multi-viewpoint front elevation gauss plane gauss coordinates system simulation space

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Applied Graph Theory in computer vision and pattern recognition 2007

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丛书名： Studies in Computational Intelligence

2007年

作者： Abraham Kandel Horst Bunke Mark Last

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