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International Conference on Machine Vision (ICMV 2007)

作者： Abdul Bais Robert Sablatnig Yahya M. Khawaja Ghulam M. Hassan Department of Computer Systems Engineering NWFP University of Engineering and Technology Peshawar Pakistan Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

This paper presents a hybrid approach towards self-localization of tiny autonomous mobile robots in a known but highly dynamic environment. The proposed algorithm is intended for two-wheeled differential drive robots which are equipped with a pivoted stereo vision system, two digital encoders, a gyro sensor, two 10g accelerometers and a magnetic compass. The global position of the robot can be estimated by extracting two distinct landmarks from the robot environment and measuring their range and orientation using the stereo vision system. However, distinct landmarks are not available through the entire state space and it is required to track the robot position once a global estimate is available. Tracking of the globally estimated position is performed within the framework of extended Kalman filter. Constant monitoring of the robot observation enables it to detect any unexpected situation. Simulation results show that robot can successfully localize itself at startup and is capable of detecting and recovering from localization failures.

关键词： Mobile robots Orbital robotics Robot sensing systems Robot vision systems Stereo vision Magnetic sensors Sensor systems Accelerometers Position measurement State-space methods

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A Flexible Fully-Multiplicative Orthogonal-Group Based ICA Algorithm

A Flexible Fully-Multiplicative Orthogonal-Group Based ICA A...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Yalan Ye Jing Wan Zhi-Lin Zhang Chen Jia Wu Lei School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

In this paper, we propose a flexible fully-multiplicative orthogonal-group based ICA (FlexibleOgICA) algorithm, which can instantaneously separate the mixture of sub-Gaussian and super-Gaussian source signals. It adopts a self-adaptive nonlinear function, which adjusts its parameter to achieve better performance based on the estimation of the kurtosis of super-Gaussian source signals. We also have successfully applied the algorithm to obtain the fetal electrocardiogram (FECG) signal, showing its fast convergence speed and high separation performance

关键词： Independent component analysis Signal processing algorithms Convergence Computational intelligence Bioinformatics Computational biology Signal processing Multidimensional signal processing Biomedical signal processing Switches

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Adaptive wavelet thresholding method for image denoising

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Guangdian Gongcheng/Opto-Electronic Engineering 2007年第6期34卷 77-81页

作者： Liu, Cheng-Yun Chen, Zhen-Xue Ma, Yu-Tao Wuhan University of Science and Technology Wuhan 430081 China State Education Commission Laboratory for Image Processing and Intelligence Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China

An improved adaptive wavelet thresholding method for image denoising was proposed to overcome the limitation of Donoho's VisuShrink and Lakhwinder Kaur's NormalShrink. According to the different sub-band characteristics, a new scale parameter equation was defined based on Lakhwinder Kaur's NormalShrink threshold, which was employed to determine the optimal thresholds for each step scale. Experimental results on several testing images show that the proposed method separates signals from noise completely in each step scale and eliminates white Gaussian noise effectively. In addition, the method also preserves the detailed information of the original image well, obtain superior quality image and improves Peak Signal to Noise Ratio (PSNR). Furthermore, since this method can improve the efficiency of image denoising and doesn't increase time complexity, it could be applied in the real-time processing.

关键词： Adaptive threshold value Denoising image processing Wavelet transform

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Perceptual Contrast-Based image Fusion: A Variational Approach

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自动化学报 2007年第2期33卷 132-137页

作者： WANG Chao YE Zhong-Fu Institute of Statistical Signal Processing Department of Electrical Engineering and Information Science University of Science and Technology of China Hefei 230027 P.R.China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 P.R.China

本地对比，或变化，在主要是从来源图象保存重要信息到熔化结果的图象熔化起一个重要作用。威伯的法律告诉我们在不同背景下面的一样的变化将引起不同感性的感情，因此，一个理想的图象处理器不得不考虑视觉心理学和心物学的效果。这份... 详细信息

本地对比，或变化，在主要是从来源图象保存重要信息到熔化结果的图象熔化起一个重要作用。威伯的法律告诉我们在不同背景下面的一样的变化将引起不同感性的感情，因此，一个理想的图象处理器不得不考虑视觉心理学和心物学的效果。这份报纸考虑人的视觉系统(HVS ) 的性质并且从每幅来源图象把量的感性的变化转移到结果。把 just-noticeable-difference (JND ) 用作测量， multiband 图象的感性的对比作为一个目标被获得。我们构造一个功能的极值问题发现一幅单身的乐队图象，或熔化结果，它有最近感性的对比到目标一个。经由变化途径， Euler-Lagrange 方程被导出，并且一次坡度降下重复被采用。试验性的结果证明这个方法感性地好。

关键词：表象聚变变化方法知觉衬比度 HVS PDES 人工智能

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Remote sensing image matching based on corner structures

Remote sensing image matching based on corner structures

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International Conference on image and Graphics (ICIG)

作者： Hongya Tuo Zhongliang Jing Yuncai Liu Institute of Aerospace Sci. & Tech. Shanghai Jiaotong University Shanghai China Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed in detail. Then some measures are given to eliminate the false corners. Finally a relaxation matching scheme is presented. The experiments show the effectiveness and feasibility of our matching method.

关键词： Remote sensing image matching Arm Computer vision Detectors Bismuth pattern recognition image edge detection Graphics image processing

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Color Active Contour Models Based Tongue Segmentation in Traditional Chinese Medicine

Color Active Contour Models Based Tongue Segmentation in Tra...

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The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

作者： Jie Yang Yonggang Wang Shengyang Yu Yan Zhang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Dept.of Dept of Computer Science Nanjing University of Science & Technology Nanjing China

Automated tongue image segmentation in tongue diagnosis system of traditional Chinese medicine is difficult due to two factors: There are lots of pathological details on the surface of tongue, and the shapes of tongue bodies are quite different By adequately considering color information of tongue images, knowledge-based initial tongue body boundary detection and a color gradient are introduced into the gradient vector flow snake. The roughly detected tongue body boundary is employed to lead the usage of color gradient and the convergence of *** experiments results show robustness and accuracy of the algorithm. This work establishes a solid foundation for feature analysis of tongue diagnosis.

关键词： tongue diagnosis tongue segmentation gradient vector flow (GVF) color gradient active contours

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Face recognition Research Based on Anti-Symmetrical Wavelet and Eigenface

Face Recognition Research Based on Anti-Symmetrical Wavelet ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qian Yin Zhi-Yong Yuan Ying Kong Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science Wuhan University of China Wuhan China School of Computer Science & Technology Beijing Institute of Technology Beijing China

In this paper, a new human face recognition method based on anti-symmetrical biorthogonal wavelet transformation (ASBWT) and eigenface was proposed. First the anti-symmetrical biorthogonal wavelet is chosen to degrade the face image dimension, meanwhile complete the process of face location and segmentation; And then human face is reverted through the face space of eigenface, the traditional average human face is replaced in the within-class scatter matrix. This within-class scatter matrix is used to calculate within-class and between-class distance proportion as a rule function, calculate the twice eigenface through discrete Karhunen-Loeve transform (DKLT), and use singular value decomposition (SVD) method to calculate the eigenvector. Finally we compute the weights and classify the face images. The results show that the proposed method has higher recognition rate and more robust than the traditional eigenface analysis method.

关键词： Face recognition Scattering Matrix decomposition image coding Humans Matrices Degradation Robustness Wavelet analysis Fourier transforms

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A comparision of Model-based methods for knee cartilage segmentation

A comparision of Model-based methods for knee cartilage segm...

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2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007

作者： Cheong, J. Faggian, N. Langs, G. Suter, D. Cicuttini, F. Dept. of Electrical Dept. of Computer Systems Engineering Monash University Australia Clayton School of Information Technology Monash University Australia Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria Dept. of Epidemiology Dept. of Preventive Medicine Monash University Australia

Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.

关键词： Medical imaging

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A robust infrared face recognition method based on adaboost gabor features

A robust infrared face recognition method based on adaboost ...

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International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Di Huang Yun-Hong Wang Yi-Ding Wang Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China Chinese Academy of Sciences Beijing China

Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature are far from perfect and deteriorate ungracefully where lighting condition cannot be controlled. This paper presents a new robust method for face recognition under near infrared lighting condition based on AdaBoost Gabor features with linear discriminant analysis classification (ALGabor), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of environmental lighting, and Gabor wavelets can extract adequate features form the images. To gain the qualified NIR images, a device has been designed. Gabor wavelets are used to extract the features form the NIR images. Although Gabor feature vectors often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative feature. Compared with the huge number of features produced by typical Gabor wavelets, the classifier in this paper only selects hundreds of features, which saves computation and time cost significantly. The comparison between the results of the method in this paper and several classic algorithms proves the effectiveness of the proposed method.

关键词： Robustness Face recognition Feature extraction Biometrics Authentication Lighting control Infrared imaging Linear discriminant analysis Wavelet analysis Computational efficiency

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A CI feature-based pulmonary nodule segmentation using three-domain mean shift clustering

A CI feature-based pulmonary nodule segmentation using three...

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International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Sheng-Dong Nie Li-Hong LI Zhao-Xue Chen Institute of Medical Imaging Engineering School of Medical Instrumentation & Foodstuff University of Shanghai for Science and Technology Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

A novel and more effective algorithm used for segmenting pulmonary nodules in thoracic spiral CT images was presented. The algorithm is based on mean shift clustering method and CI (Convergence Index) features, which can represent the multiple Gaussian model of pulmonary nodules both for solid and sub-solid, substantially. The algorithm has the following steps: (1) calculating the CI features of all pixels in the region of interest (ROI), (2) combining the CI features with the intensity range and the spatial position of the pixels to form a feature vector set, (3) grouping the feature vector set to clusters with mean shift clustering algorithm. Owing to our algorithm can represent the multiple Gaussian model both for solid and sub-solid nodules, it can be used in any user interested nodule regions, especially suitable for the segmentation of sub-solid nodules. Experiments demonstrated that our algorithm can figure out the outline of pulmonary nodules of different forms more precisely.

关键词： Clustering algorithms image segmentation Lungs Computed tomography Cancer pattern recognition Biomedical imaging Solid modeling Wavelet analysis image analysis

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