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Conference on pattern recognition and computer vision

作者： Ming Delie Tian Jinwen Liu Jian 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

Human chromosome karyotyping is one of the essential tasks in cytogenetics, especially in genetic syndrome diagnoses. In this thesis, an automatic procedure is introduced for human chromosome image analysis. According to different status of touching and overlapping chromosomes, several segmentation methods are proposed to achieve the best results. Medial axis is extracted by the middle point algorithm. Chromosome band is enhanced by the algorithm based on multiscale B-spline wavelets, extracted by average gray profile, gradient profile and shape profile, and calculated by the WDD (Weighted Density Distribution) descriptors. The multilayer classifier is used in classification. Experiment results demonstrate that the algorithms perform well.

关键词： chromosome image processing segmentation centromere locating band pattern description

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Simulation of sea surface images in the infrared and similarity evaluate

Simulation of sea surface images in the infrared and similar...

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Conference on pattern recognition and computer vision

作者： Yang, Jiangbo Wang, Yuehuan Zhang, Tianxu Huazhong Univ Sci & Technol State Key Lab Multispectral Informat Proc Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

The simulation of ocean environment in the infrared has been a hot yet difficult problem in the field of computer simulation. In this paper, the shortage of the simulation of infrared ocean images with Vega is analyzed, and then a new simulation method based on 3D modeling with OpenGL is introduced. The new method abandons the high precision mesh but uses mathematical model to manipulate vertex of the mesh and establish the model. Experiments demonstrated that the method proposed is much more efficient and guarantees the quality of the simulation images. Finally a similarity evaluation function based on features extracted from co-occurrence matrix such as angular second moment, entropy, related coefficient, contrast and uniformity is put forward to evaluate the similarity of the images.

关键词： scene simulation IR image similarity evaluation OpenGL

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Classification method based on KCCA

Classification method based on KCCA

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Conference on pattern recognition and computer vision

作者： Wang, Zhanqing Zhang, Guilin Zhao, Guangzhou Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China Wuhan Univ Technol Coll Sci Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Dept Elect & Informat Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

Nonlinear CCA extends the linear CCA in that it operates in the kernel space and thus implies the nonlinear combinations in the original space. This paper presents a classification method based on the kernel canonical correlation analysis (KCCA). We introduce the probabilistic label vectors (PLV) for a give pattern which extend the conventional concept of class label, and investigate the correlation between feature variables and PLV variables. A PLV predictor is presented based on KCCA, and then classification is performed on the predicted PLV. We formulate a frame for classification by integrating class information through PLV. Experimental results on Iris data set classification and facial expression recognition show the efficiencies of the proposed method.

关键词： CCA KCCA probabilistic label vector (PLV) classification

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Building detection from LIDAR and images in urban areas

Building detection from LIDAR and images in urban areas

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Conference on pattern recognition and computer vision

作者： Guan, Haiyan Deng, Fei Zhang, Jianqing Wuhan Univ Sch Remote Sensing Informat Engn 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

ISBN: (纸本)9780819469526

Rapid texture mapping of buildings is a key aspect for reconstruction of 3D city landscapes. An effective approach by the way of coarse-to-fine 3D building model generation by integration of LIDAR and multiple overlap images is proposed. Classification and segmentation can be processed by combined multi-spectral information which is provided by color aerial image and geometric information from multi-return laser scanned data. A connected graph of the segment label image has to be created to derive the neighborhood relation of the planar segments. A line segment matching, based on geometry and chromatic constraint, is applied for automatically getting the corresponding line features in multi target images. Hypotheses for polyhedral surfaces are selected using topological relations and verified using geometry.

关键词： LIDAR building detection modelling pattern recognition

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Object detection based on 2D canonical correlation analysis

Object detection based on 2D canonical correlation analysis

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Conference on pattern recognition and computer vision

作者： Zhang, Guofeng Zhou, Weida Ren, Weihua Liu, Shuang Xidian Univ Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819469526

A novel approach combining 2DCCA, edge detector, and corner detector for object detection is proposed in this paper. The detection system consists of two stages. In the first stage, edge and corner information is obtained by edge detector and corner detector. By setting range for the number of edge pixel and corner in the scanning window, a large number of non-object windows are rejected. In the second stage, the classifier trained by 2DCCA is combined with slide window method so that further non-object windows are rejected. For the case that one object is simultaneously contained in several windows, the algorithm of determining the best position of object is designed. Compared with related approaches, our method has advantage of obtaining higher precision under the similar recall. The performance of the proposed approach is illustrated by experimental results.

关键词： object detection 2DCCA machine learning pattern recognition

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Improved neighborhood preserving embedding approach

Improved neighborhood preserving embedding approach

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Conference on pattern recognition and computer vision

作者： Zhi, Ruicong Ruan, Qiuqi Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)9780819469526

In this paper.. we proposed a manifold-based algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE) for dimensionality reduction and feature extraction. ONPE algorithm is based on the Neighborhood Preserving Embedding (NPE) algorithm. NPE is an unsupervised dimensionality reduction method which is the linear approximation of classical nonlinear method. However, the feature vectors obtained by NPE are nonorthogonal. ONPE inherits NPE's neighborhood preserving property and produces orthogonal feature vectors. As orthogonal eigenvectors preserve the metric structure of the image space, the ONPE algorithm has more neighborhood preserving power and discriminating power than NPE. Furthermore, ONPE can find the mapping which best preserves the manifold's estimated intrinsic geometry structure in a linear sense. Experimental results show that ONPE is an effective method for feature extraction.

关键词： Orthogonal Neighborhood Preserving Embedding Neighborhood Preserving Embedding dimensionality reduction pattern recognition

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Efficient stellar spectral type classification for SDSS based on nonnegative matrix factorization

Efficient stellar spectral type classification for SDSS base...

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mippr 2007: pattern recognition and computer vision

作者： Yang, Jinfu Liu, Zhongtian Wu, Fuchao School of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9780819469526

The problem of identifying spectra collected by large sky survey telescope is urgent to study to help astronomers discover new celestial bodies. Due to spectral data characteristics of high-dimension and volume, principle component analysis (PCA) technique is commonly used for extracting features and saving operations. Like many other matrix factorization methods, PCA lacks intuitive meaning because of its negativity. In this paper, non-negative matrix factorization (NMF) technique distinguished from PCA by its use of nonnegative constrains is applied to stellar spectral type classification. Firstly, NMF was used to extract features and compress data. Then an efficient classifier based on distance metric was designed to identify stellar types using the compressed data. The experiment results show that the proposed method has good performance over more than 70,000 real stellar data of Sloan Digital Sky Survey (SDSS). And the method is promising for large sky survey telescope projects.

关键词： Object recognition

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A note on the convergence of the mean shift

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pattern recognition 2007年第6期40卷 1756-1762页

作者： Li, Xiangru Hu, Zhanyi Wu, Fuchao Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

Mean shift is an effective iterative algorithm widely used in computer vision community. However, to our knowledge, its convergence, a key aspect of any iterative algorithm, has not been rigorously proved up to now. In this paper, by further imposing some commonly acceptable conditions, its convergence is proved. (c) 2006 Published by Elsevier Ltd on behalf of pattern recognition Society.

关键词： mean shift convergence local structure computer vision

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Fuzzy-rough membership function neural network and its application to pattern recognition

Fuzzy-rough membership function neural network and its appli...

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Conference on pattern recognition and computer vision

作者： Zhang, Dongbo Wang, Yaonan Huang, Huixian Xiangtan Univ Inst Informat Engn Xiangtan 411105 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9780819469526

Generally, while designing pattern classifier, the boundaries between different classes are vague and it is often difficult or impossible to acquire all of the necessary essential features for precisely classifying, so often both the fuzzy uncertainty and rough uncertainty are exist in classification problems. In this work, a novel FRMFN (Fuzzy-Rough Membership Function Neural Network) is built based on fuzzy-rough sets theory. The FRMFN integrates the ability of processing fuzzy and rough information simultaneously. The test results of classification for infrared band combination image of Canada Norman Wells area and five vowel characters indicate that FRMFN has better classification precision than RBFN (Radial Basis Function Neural Network) and has the same merit of quick learning as RBFN.

关键词： fuzzy-rough sets fuzzy-rough membership function RBF neural network fuzzy-rough membership function neural network pattern recognition

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Time-stamped planar curves matching for reconstruction of aerocraft trajectories

Time-stamped planar curves matching for reconstruction of ae...

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Conference on pattern recognition and computer vision

作者： Wang, ShuWen Wang, Jin Zhang, TianXu Zhao, GuangZhou Radar Acad Dept Early Warming Command Wuhan 430019 Peoples R China Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Dept Elect & Informat Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9780819469526

Planar mapped trajectories (curves composed of time-stamped points) matching differs to other planar curves correspondence in stereo vision. Most methods proposed for general planar curves matching are not much suitable for it. In this paper, a new coarse-to-fine method for planar trajectories is proposed;some physical constraints such as time, height, speed limitation and continuousness of aerocraft trajectories are used to make matching process more reasonable. Similarity Measures between left and right images planar trajectories are calculated for matching, flight trajectories are reconstructed at last, and reconstruction error is also discussed at last.

关键词： curve matching flight trajectories reconstruction physical constraints binocular stereo stereo vision

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