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International Conference on computer science and Software Engineering, CSSE 2008

作者： Hualong, Zhao Hongshi, Sang Tianxu, Zhang Yebin, Fan State Key Lab. for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769533360

Two of the most significant factors in the success of today's system-on-chip (SoC) designs are the ability to deliver efficient access to off-chip high speed memory and the ability to be compatible with several different interface-timings of several different sorts of memories. And the scheduling scheme of data streams for multi-master and multi-outstanding requests situation in complex SoC should be considered carefully, too. This paper introduces a novel general external memory interface (GEMI) architecture for the high performance and high flexibility memory interface. Several techniques and methods, i.e. data and command buffers based on asynchronous FIFO, optimized bank management, multi-port access scheduler, request reorder unit, are proposed in this paper for fulfilling the requirements mentioned above. Some techniques are specially proposed for data streams scheduling in complex SoC. At the end of the paper, the simulation and tape-out results are provided. The whole implementation of architecture is proven to be not only functional and efficient, but also flexible and reusable by simulation and silicon verification. © 2008 IEEE.

关键词： Scheduling

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MLPP: A modified version of locality preserving projection

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Journal of Computational Information Systems 2008年第5期4卷 1891-1896页

作者： Zheng, Zhonglong Zhang, Haoran Yang, Fa Yang, Jie Department of Computer Science Zhejiang Normal University Jinhua 321004 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

Locality Preserving Projection (LPP), as a linear manifold learning algorithm, has attracted much interests in recent years. LPP considers an n1× n2image as a vector in €n1×n2space, and thus is limited by the curse of dimensionality. To deal with such problem, Principal Component Analysis (PCA) is traditionally used for dimensionality reduction first. However, some discriminant information would be discarded by PCA. In this paper, a modified version of LPP (MLPP) is proposed which can be applied directly in the original face space without the aid of PCA. Moreover, the mapping matrix derived from MLPP is orthogonal in contrast to that of LPP. Experiments show competitive performance of our method. © 2008 Binary Information Press.

关键词： Principal component analysis

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Needle segmentation using 3D quick randomized hough transform

Needle segmentation using 3D quick randomized hough transfor...

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1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Institute for Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9780769533919

A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D Quick Randomized Hough Transform (3DGHT), which is based on the 3D Randomized Hough Transform and coarse-fine searching strategy. We tested it with water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1 and position deviation less than 1mm, and the computational time of segmentation with 35MB data is within 1s. © 2008 IEEE.

关键词： Hough transforms

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SVM based ASM for facial landmarks location

SVM based ASM for facial landmarks location

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8th IEEE International Conference on computer and Information Technology (CIT 2008), vol.1

作者： Chunhua Du Qiang Wu Jie Yang Zheng Wu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Computer Systems University of Technology Sydney Australia

Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence.

关键词： Active shape model (ASM) Support vector machine (SVM) Multi-class classification Facial landmark New position

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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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Extraction of complex wavelet features for iris recognition

Extraction of complex wavelet features for iris recognition

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International Conference on pattern recognition

作者： Xiaofu He Pengfei Shi Department of Computer Science and Technology East China Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shandong Jiaotong University Shanghai China

ISBN: (纸本)9781424421749

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transformpsilas properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate.

关键词： Iris recognition Feature extraction Wavelet analysis Wavelet transforms image resolution image analysis image reconstruction Two dimensional displays Frequency Encoding

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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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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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Voronoi-Based Sleeping Configuration in Wireless Sensor Networks with Location Error

Voronoi-Based Sleeping Configuration in Wireless Sensor Netw...

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IEEE International Conference on Networking, Sensing and Control

作者： Xinyu Chen Michael R. Lyu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Department of Computer Science and Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In this paper, we present a Voronoi-based sleeping configuration to deal with different sensing radii and location error. With our proposed sleeping candidate condition, redundant sensors are optionally identified and scheduled to sleep in order to extend the system lifetime while maintaining adequate sensor redundancy to tolerate sensor failures, energy depletions, and location error. Simulation results show that there is a tradeoff among energy conservation, area coverage, and fault tolerance, which varies between different sleeping candidate conditions.

关键词： Wireless sensor networks Sensor systems Surveillance Quality of service Redundancy Energy conservation Fault tolerance Sensor phenomena and characterization Batteries image processing

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