检索结果-内蒙古大学图书馆

Improved global optical flow estimation with mean shift algorithm for target detection

International Journal of Wireless and Mobile Computing 2015年第4期8卷 323-328页

作者： Wang, Zhonghua Li, Chunyong Liu, Qingping Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China

In view of the problem that the global optical flow algorithm cannot acquire accurate motion parameter estimation at a low-gradient value, an improved method has been presented in order to enhance the self-adaptive ability. The proposed method is divided into three steps: firstly, the Gaussian filter is used to restrain the background noise;secondly, the optical basic constraint weighted function is modified to acquire targets area;finally, the morphological filtering and mean shift segmentation algorithm is adopted to reduce the false alarm rate. Compared with other algorithms, the experiment results show that the proposed method can improve the self-adaptive ability and the detection rate. © 2015 Inderscience Enterprises Ltd.

关键词： Optical flows

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Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Shape driven kernel adaptation in Convolutional Neural Netwo...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Shaoxin Li Junliang Xing Zhiheng Niu Shiguang Shan Shuicheng Yan Department of Electrical and Computer Engineering National University of Singapore Singapore Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China

ISBN: (纸本)9781467369657

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

关键词： Kernel Shape Face Face recognition Robustness Neural networks Computer architecture

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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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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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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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Detection level of mango based on neural network and digital image

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Journal of Chemical and Pharmaceutical Research 2014年第2期6卷 129-132页

作者： Li, Xiaoling Yuan, Jimin School of Computer Science and Technology of Chengdu University China University Key Laboratory of Pattern Recognition and Intelligent Information Processing China Department Computer Engineering of Chengdu Aeronautic Vocational and Technical College China

In view of the draw backs of mango grade identification in China, which still relies on photoelectric sorting and manual separation, this paper presents a processing method on the basis of the technology of computer vision and digital image. Utilizing image processing technology, the researcher calculated the length of the long-short-axis, marked the location of it and calculated the 7 parameters, chroma, length, width and etc.,4 of which are chosen as the key characteristics of the BP input of network to build a network and identify the level of mango through analysis of the external characteristics of mango. The method is based on traditional characteristics detection, using boundary tracking algorithm and the length of the new long-short-axis detection algorithm. The result of experiment indicates that the calculating method and judging of the level of mango are precise and accurate, with an average recognition rate of 92%. Therefore, the method has a great practical value, which can be applied to other agricultural products classification.

关键词： Fruits

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Noise reduction based on robust principal component analysis

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Journal of Computational information Systems 2014年第10期10卷 4403-4410页

作者： Sun, Chengli Zhang, Qin Wang, Jian Xie, Jianxiao Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China School of Information Nanchang Hangkong University Nanchang 330063 China

In this paper, we present a new speech enhancement method based on robust principal component analysis. In the proposed method, noisy signal is transformed into time-frequency domain where background noise is assumed as a low-rank component and human speech is regarded as a sparse compone. An inexact augmented Lagrange multipliers algorithm is conducted for solving the noise and speech separation problem. Experimental results show the RPCA based speech enhancement method can steadily obtain higher noise suppression performance in noisy conditions, compared to many traditional methods. 1553-9105/Copyright © 2014 Binary information Press.

关键词： Lagrange multipliers

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A new network traffic prediction algorithm based on discrete queue

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Journal of Computational information Systems 2015年第12期11卷 4255-4261页

作者： Zhang, Hong Hu, Jun Shen, Yuncheng Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu China College of Information Science and Technology Chengdu University Chengdu China College of Computer Science Sichuan University Chengdu China School of Traffic and Transportation Beijing Jiaotong University Beijing China Faculty of Computer Science Chengdu Normal University Chengdu China College of Information Science and Technology Zhaotong University Zhaotong China

In order to verify the network traffic decline because by node breakdown, this paper proposes a new type of prediction algorithm (Prediction algorithm based on Discrete-Queue for FARIMA model, PDF). At first, the mathematic formula for queuing situation and average queue length in steady state is derived with queuing theory in this algorithm based on discrete time, and the prediction method is established by FARIMA Model. Then, a simulation was conducted to research on the relationships between average queue length and service rate. The result shows that it has good adaptability, and the standard deviation between prediction traffic and original traffic is 10.18. ©, 2015, Binary information Press. All right reserved.

关键词： Forecasting

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A multi-dimensional perception of adaptive routing algorithm based OLSR

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Journal of information and Computational Science 2015年第8期12卷 3165-3172页

作者： Zhang, Hong Shen, Yuncheng Li, Zhongwen Hu, Jun Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu China College of Information Science and Technology Chengdu University Chengdu China College of Computer Science Sichuan University Chengdu China College of Information Science and Technology Zhaotong University Zhaotong China School of Traffic and Transportation Beijing Jiaotong University Beijing China Faculty of Computer Science Chengdu Normal University Chengdu China

Center nodes have a bigger load and burden with lots of routing in an Ad Hoc Network Model. Congestion of the nodes' packets has a great impact on network performance, especially in wireless networks. This paper proposes a multi-dimensional perception of adaptive routing algorithms based on the loading of MAC, in particular its utility. Through calculating the probabilities of packet arrival, distribution and intensity of interference, conicting nodes and adjusting the contracting strength of packets would optimize network performance. Our simulated experiments show that this algorithm improves the success rate of data transmission, reduces the delay of packet transmission and improves network throughputs. Copyright © 2015 Binary information Press.

关键词： Network performance

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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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