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第三十三届中国控制会议

作者： YANG Yawen ZHANG Peng Peng QIAO Yu YANG Jie WANG Sheng Zheng The Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong University Merchant Marine College Shanghai Maritime University

ISBN: (纸本)9781479947249

Point matching is an important component of image *** years,Coherent Point Drift(CPD) method becomes a very popular point matching *** treats point matching as a probability estimation problem and speeds up the process of matching a *** this method,one set of points are thought to be sampled from a Gaussian Mixture Model(GMM),which is centered by the other set of ***,CPD is sensitive to outliers and noises,especially when the noise ratio increased or the number of outliers gets much *** deal with this problem,we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this *** main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid,this Gaussian component should be have a more influence to ***,we set prior probability of GMM with the similarity between GMM components and the data *** the computation of similarity is based on shape *** experiments on 2D and 3D images show that when noise ratio is low,our method performs as well as CPD does,but as the ratio increased,our method is more robust and satisfactory than CPD.

关键词： CPD,Shape context,Image registration,Point matching

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Object classification via PCANet and color constancy model 4

Object classification via PCANet and color constancy model

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4th International Conference on Advanced Design and Manufacturing Engineering, ADME 2014

作者： Hu, De Kun Zhang, L. Zhao, Wei Dong Yan, Tao Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China

ISBN: (纸本)9783038352570

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images. © (2014) Trans Tech Publications, Switzerland.

关键词： Object recognition

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A Novel Ordinal Regression Method with Minimum Class Variance Support Vector Machine

A Novel Ordinal Regression Method with Minimum Class Varianc...

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2015 International Conference on Materials Engineering and Information Technology Applications(MEITA 2015)

作者： Jinrong Hu Xiaoming Wang Zengxi Huang School of Computer and Soft Engineering Xihua University Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu University

ISBN: (纸本)9781510812055

In the paper, we propose a novel ordinal regression method called minimum class variance support vector ordinal regression(MCVSVOR). MCVSVOR is derived from minimum class variance support vector machine(MCVSVM) which is a variant of SVM, and so inherits the latter's characteristics such as taking the distribution of the categories into consideration and good generalization performance. Finally, the experimental results validate the effectiveness of MCVSVOR and indicate its superior generalization performance over SVOR.

关键词： Machine learning Ordinal regression Support vector machine Support vector ordinal regression

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recognition of facial expression via kernel PCA network 3

Recognition of facial expression via kernel PCA network

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3rd International Conference on Information Technology and Management Innovation, ICITMI 2014

作者： Hu, De Kun Ye, An Sheng Li, Li Zhang, Li Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China

ISBN: (纸本)9783038352396

In this work, a kernel principle component analysis network (KPCANet) is proposed for classification of the facial expression in unconstrained images, which comprises only the very basic data processing components: cascaded kernel principal component analysis (KPCA), binary hashing, and block-wise histograms. In the proposed model, KPCA is employed to learn multistage filter banks. It is followed by simple binary hashing and block histograms for indexing and pooling. For comparison and better understanding, We have tested these basic networks extensively on many benchmark visual datasets(such as the JAFFE [13] database, the CMU AMP face expression database, a part of the Extended Cohn-Kanade (CK+) database), The results demonstrate the potential of the KPCANet serving as a simple but highly competitive baseline for facial expression recognition. © 2014 Trans Tech Publications, Switzerland.

关键词： Database systems

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Generalized quantum genetic algorithm of multidimensional space

Generalized quantum genetic algorithm of multidimensional sp...

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2014 International Conference on Logistics Engineering, Management and Computer Science, LEMCS 2014

作者： Zhang, Xiaofeng Miao, Wei Sui, Guifang Yang, Guowei Key Laboratory of JiangXi Procince for Image Processing and Pattern Recognition Information Engineering of NanChang HangKong University NanChang JiangXi330063 China

ISBN: (纸本)9789462520103

Quantum coding plays an important role in quantum genetic algorithm and affects the optimizing efficiency of algorithm, However, there are some defects in existing quantum genetic algorithm: the quantum coding scheme reduces the stability of the algorithm, making the algorithm easily occurs premature convergence and falls into local extremums. Therefore, coding scheme of multi gene chain is extended in a four dimensional space and multidimensional space based on an improved algorithm. Through simulation experiments of function extremum, combine double and triplestranded coding scheme, in accordance with the characteristics of qubits and the normalization condition. Through the experiment, study the effect of multiple genes encoding scheme chain on the performance of the algorithm. The results show that the optimal results of the algorithm can be better and the precision of solution can be higher by increasing the genetic chain. It is an effective strategy to improve the performance of algorithm. © 2014. The authors - Published by Atlantis Press.

关键词： Chains

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pattern matching with flexible wildcard gaps

Pattern matching with flexible wildcard gaps

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Junyan, Zhang Chenhui, Yang Information Science and Technology College Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China

ISBN: (纸本)9789400776173

pattern matching is a fundamental application in biomedicine and biological sequence analysis. A wildcard can match any one character in a sequence. Multiple wildcards form a gap. A flexible wildcard gap can match any characters with specific length which is specified by users. Therefore, the effective algorithm performing this kind of matching is in great need. In this paper, we design PMFG algorithm and achieve it by dividing a pattern into multiple sub-patterns with different length based on gap segmentation. After computing the starting positions and ending positions of each subpattern, the effective intervals and effective starting positions can be determined one by one. The number of the elements in the last effective position set equals to the number of the matching. A comparison experiments are done based on three DNA sequences. The results show that PMFG algorithm has better performance in the same fields. © Springer Science+Business Media Dordrecht 2014.

关键词： pattern matching

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Gait period detection based on layered coding of depth information

Gait period detection based on layered coding of depth infor...

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International Conference on Machine Tool Technology and Mechatronics Engineering, ICMTTME 2014

作者： Zhang, Hao Lin Ben, Xian Ye Zhang, Peng Liu, Tian Jiao Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Science and Engineering Shandong University Jinan China

ISBN: (纸本)9783038352464

Gait period detection, serving as a preprocessor for gait recognition, is commonly studied in the recent past. In this paper, we proposed a novel gait period detection method for depth gait video stream. The method introduces the concept of layered coding for depth images which decreases computational complexity. Furthermore, the extreme value of the sum of layered codes for gait sequence is utilized to judge the period endpoint, which is in accord with the naked-eye observation. In addition, gait recognition experiments on the TUM GAID database are conducted with the description of gait features of one single detected period by the proposed scheme using tensor representation. The high recognition accuracy verifies the effectiveness of the proposed depth gait period detection method. © (2014) Trans Tech Publications, Switzerland.

关键词： Codes (symbols)

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Combining local binary patterns for scene recognition

Journal of Software

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Journal of Software 2014年第1期9卷 203-210页

作者： Song, Minguang Guo, Ping Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Difference Binary pattern (LDBP) also was proposed and performed better than that of PACT. LDBP is based on the comparisons between center pixel and its neighboring pixels, but the relationship among neighbor pixels is not considered. In this paper, we propose to combine Local Neighbor Binary pattern (LNBP) with LDBP to construct a spatial representation for scene recognition, because that LNBP can provide complementary information regarding neighboring pixels for LDBP. Experiments on widely used datasets demonstrate that the performance of image recognition is further improved with proposed method. © 2014 ACADEMY PUBLISHER.

关键词： Pixels

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When Face recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face recognition

When Face Recognition Meets with Deep Learning: An Evaluatio...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Guosheng Hu Yongxin Yang Dong Yi Josef Kittler William Christmas Stan Z. Li Timothy Hospedales Centre for Vision Speech and Signal Processing University of Surrey UK Indicates equal contribution LEAR team Inria Grenoble Rhone-Alpes Montbonnot France Electronic Engineering and Computer Science Queen Mary University of London UK Chinese Academy of Sciences Center for Biometrics and Security Research & National Laboratory of Pattern Recognition China

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

关键词： Face recognition Face Databases Measurement Training Object recognition Convolutional codes

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Multi-view face detection based on multi-features adaboost collaborative learning algorithm

Multi-view face detection based on multi-features adaboost c...

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2014 International Conference on Applied Sciences, Engineering and Technology, ICASET 2014

作者： Huang, Rong Bing Zhang, Hong Shu, Chang Ming Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Sichuan 610106 China School of Information Science and Technology Chengdu University Sichuan 610106 China

ISBN: (纸本)9783038351849

In view of the multi-view face detection problem under complex background, an improved face detection method based on multi-features boosting collaborative learning algorithm integrating local binary pattern (LBP) is presented. Firstly, facial skin color information is used to exclude most of the background regions. Then, Haar-like feature and LBP feature are extracted from facial candidate regions and inputted into a modified AdaBoost algorithm to obtain a strong classifier. Lastly, in order to improve the detection speed, pyramid classifier system structure is adopted to determine the face. The experimental results on CMU standard test set and life photos, the proposed method has achieved the rapid detection of multi-view face image. © (2014) Trans Tech Publications, Switzerland.

关键词： Face recognition

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