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International Conference on Intelligent Computation Technology and Automation, ICICTA

作者： Yazhuo Gong Pengfei Shi Institute of Digital Printing University of Shanghai for Science and Technology Shanghai China Institute of Image Processing & Pattern recognition Shanghai Jiaotong University Shanghai China

This paper presents a new reconstruction algorithm of iris image with great depth of field. First, the focus issue in image capture process is proposed, and characteristic of multi-focus iris image is discussed, which proves the feasibility of reconstruction of iris image with great depth of field based on multi-focus fusion. The proposed method uses antisymmetric biorthogonal wavelet to detect the iris texture edge, and according to the density of edge pixels, chooses the iris sub-regions of high articulation for image fusion. Finally, experimental data set compared with traditional non-fusion method is presented, and the proposed method shows its advantages in performance and practical value in registration process.

关键词： Reconstruction algorithms Focusing Lenses Cameras image edge detection Forward contracts Gray-scale Iris recognition Apertures Muscles

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Gabor-Based Discriminative Common Vectors for Gait recognition

Gabor-Based Discriminative Common Vectors for Gait Recogniti...

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Congress on image and Signal processing, CISP

作者： Xiaochao Yang Ji Dai Yue Zhou Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China School of Information and Electrical Engineering China University of Mining and Technology China

This paper presents a novel method for identity recognition based on the 2D gait representation: Gait Energy image (GEI) which is the averaged silhouette over one gait cycle. An ensemble of Gabor kernels is first convolved with GEI to extract discriminative feature. The obtained Gabor gait representation is then projected into lower dimensional subspace using discriminative common vectors (DCV) analysis. The final classification is performed in this subspace. The proposed method is tested on the USF HumanID Database. Experimental results show that Gabor-based method can improve recognition rate, and DCV is superior to other traditional dimensional reduction algorithm in the gait recognition application.

关键词： image recognition Biometrics Legged locomotion Hidden Markov models pattern recognition Kernel Joints image processing Feature extraction Video sequences

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Subspace Analysis Methods plus Motion History image for Human Action recognition

Subspace Analysis Methods plus Motion History Image for Huma...

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Chunhua Du Qiang Wu Jie Yang Xiangjian He Yan Chen Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China School of Computing & Communications Sydney University of Technology Broadway Australia

This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method.

关键词： image analysis image motion analysis Motion analysis History Humans image recognition image coding Feature extraction image sequence analysis Databases

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Extracting key postures using radon transform

Extracting key postures using radon transform

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International Conference on image and Vision Computing New Zealand, IVCNZ

作者： Yan Chen Qiang Wu Xiangjian He Chunhua Du Jie Yang Centre for Innovation in IT Services and Application (iNext) University of Technology Australian Institute of Image Processing and Pattern Recognition Shandong Jiaotong University Shanghai China

Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video based on Radon transform. Cluster is used on the Radon transform to select the final key postures of human action video. The approach does not require motion extraction from the human action video. The experiments results show that the proposed approach is efficient.

关键词： Humans Data mining pattern recognition Entropy Technological innovation image processing Shape Speech analysis Information retrieval Tracking

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Novel way achieving 3D reconstruction of actual human face using multi images

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2008年第4期36卷 661-666页

作者： Hou, Wen-Guang Chan, Tai-Wai Ding, Ming-Yue Image Processing and Intelligent Control Key Laboratory Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Industrial Centre Hong Kong Polytechnic University Hong Kong Hong Kong

A novel way achieving geometrical reconstruction of actual human face through projecting two types of texture on face in short time is advanced. The first type texture is stripe which is used to establish parallax grid between images. Taking into account of its results, the second type projecting texture is used to match by virtue of its abundant traits. After realizing geometrical reconstruction, the paper provides a general way about achieving actual texture reconstruction by the outer spherical surface surrounding object. In order to uniform color, it deals with parts of images in conjunct region and makes the color change meeting a certain function on condition of keeping their original information mostly. Results show this way can improve reconstruction quality and decrease complicacy of algorithm.

关键词： image reconstruction

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Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral Remote Sensing image

Ant Colony Optimization Algorithm for Feature Selection and ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lintao Wen Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if we only consider the image's spectral feature or texture feature alone. In this paper, we present a new approach by applying the Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to classify the multispectral remote sense image. Using the combination of the spectral and texture features obtained by ACO in classification always produces a better accuracy.

关键词： Ant colony optimization Remote sensing Remote monitoring Multispectral imaging image analysis Data mining Feature extraction Entropy image processing pattern recognition

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Experimental Study of Discriminant Method with Application to Fault-Prone Module Detection

Experimental Study of Discriminant Method with Application t...

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International Conference on Computational Intelligence and Security

作者： Gege Guo Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781424441969

Some techniques have been applied to improving software quality by classifying the software modules into fault-prone or non fault-prone categories. This can help developers focus on some high risk fault-prone modules. In this paper, a distribution-based Bayesian quadratic discriminant analysis (D-BQDA) technique is experimental investigated to identify software fault-prone modules. Experiments with software metrics data from two real projects indicate that this technique can classify software modules into a proper class with a lower misclassification rate and a higher efficiency.

关键词： Fault detection Software quality Support vector machines Support vector machine classification pattern recognition Software metrics Bayesian methods Fault diagnosis Software performance Computational intelligence

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Gabor phase embedding of Gait Energy image for identity recognition

Gabor phase embedding of Gait Energy Image for identity reco...

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International Conference on Computer and Information Technology (CIT)

作者： Xiaochao Yang Tianhao Zhang Yue Zhou Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Computing Hong Kong Polytechnic University Hong Kong China

This paper proposes an effective gait recognition approach based on the Gait Energy image (GEI) representation. Synthetic GEI samples are first generated to address the problem of lacking training data. Then the Gabor phase spectrum of GEI which was ignored in the previous work is utilized as the input feature, and it is subsequently embedded into a low dimensional manifold by locality preserving projections to perform classification. The proposed recognition approach is evaluated on the USF HumanID database. Experimental results show that our approach outperforms other automatic algorithms in terms of recognition accuracy.

关键词： Principal component analysis Probes Training Kernel Convolution Face Databases

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Comparison of discriminant analysis methods applied to diffractive optically variable image

Comparison of discriminant analysis methods applied to diffr...

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IEEE International Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Rukun Hu Youji Feng Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China The School of Computer Science and Technology Beijing Institute of Technology Beijing China

As a kind of powerful anti-counterfeiting device, diffractive optically variable image (DOVI) has been developed and widely used in information security field. However, the identification of DOVI today by bare eyes is not reliable. In this paper we investigate the recognition of DOVI with machine learning method, and five kinds of algorithms, namely quadratic discriminate analysis (QDA), linear discriminate analysis (LDA), regularized discriminate analysis (RDA), leave-one-out covariance matrix estimate (LOOC), and Kullback-Leibler information measure based method (KLIM) are applied to the recognition of DOVI. Considering both time cost and correct classification rate, KLIM classifier exceeds others.

关键词： image analysis Optical diffraction Information analysis Algorithm design and analysis Optical devices Information security Eyes Learning systems Machine learning algorithms Linear discriminant analysis

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Software quality prediction using Affinity Propagation algorithm

Software quality prediction using Affinity Propagation algor...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bingbing Yang Qian Yin Shengyong Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Laboratory of Computer Science Institute of Software Chinese Academy and Sciences Beijing China

Software metrics are collected at various phases of the software development process. These metrics contain the information of the software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which can be considered as one of the data mining techniques, is adopted to build the software quality prediction models in the early period of software testing. In this paper, a new clustering method called Affinity Propagation is investigated for the analysis of two software metric datasets extracted from real-world software projects. Meanwhile, K-Means clustering method is also applied for comparison. The numerical experiment results show that the Affinity Propagation algorithm can be applied well in software quality prediction in the very early stage, and it is more effective on reducing Type II error.

关键词： Software Software quality Clustering methods Clustering algorithms Software metrics Testing Software algorithms

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