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Robust ICA neural network and application on Synthetic Aperture Radar (SAR) image analysis

引用

13th International Conference on Neural Information processing, ICONIP 2006

作者： Jian, Ji Zheng, Tian Department of Computer Science and Technology Northwestern Polytechnical University Xi'an 710072 China Department of Applied Mathematics Northwestern Polytechnical University Xi'an 710072 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)3540464816

Independent component analysis (ICA) has shown success in the separation of sources in lots of applications. However, in synthenic aperture radar (SAR) images the noise is multiplicative, so the applicability of ICA is seriously reduced. This paper proposes a new robust independent component analysis neural network (RICANN) that improves the robustness of ICA by adding outlier rejection rule. Its application in synthetic aperture radar (SAR) is discussed. The results show the potential usage in SAR image processing problems. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Synthetic aperture radar

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Speckle reduction of polarimetric SAR images based on neural ICA

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13th International Conference on Neural Information processing, ICONIP 2006

ISBN: (纸本)3540464816

The polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For the scene interpretation, it is desirable to separate between the target response and the speckle. For this purpose, we proposed a new speckle reduction approach using independent component analysis (ICA) based on statistical formulation of PSAR image. In addition, we apply four ICA algorithms on real PSAR images and compare their performances. The comparison reveals characteristic differences between the studied neural ICA algorithms, complementing the results obtained earlier. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Synthetic aperture radar

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TARGET RECOGNITION USING BAYESIAN DATA FUSION METHOD

TARGET RECOGNITION USING BAYESIAN DATA FUSION METHOD

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YU-QIU SUN JIN-WEN TIAN JIAN LIU School of Information and Mathematics Yangtze University Jingzhou 434023 China State Education Co State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute fo State Education Commission Key Laboratory for Image Processing and Intelligent Control Dept.of Elec

Multisensor information plays an important pole in the target recognition and other application fields. Fusion performance is tightly depended on the fusion level selectes and the approach used. Feature level fusion is a potential and difficult fusion level. Bayesian fusion method is an important theory in feature level. A new method is presented to fuse infrared images and recognize object in the paper. Firstly,Bayesian principles, fusion mode and recognition decision function are described. Then, aiming at the features of mid-wave infrared image and long-wave infrared image, we use Bayesian probability to fuse them. Last, recognize target and background obtained with training and test pattern vectors. The experiment results show stability and feasibility of the fusion recognition using Bayesian decision theory in infrared image.

关键词： Bayesian decision data fusion target recognition infrared images

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Contrast-modulated Nonlinear Diffusion for X-ray Angiogram images

Contrast-modulated Nonlinear Diffusion for X-ray Angiogram I...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Baopu Li Nong Sang Zhiguo Cao Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan China

In this paper, we present a new method for X-ray angiogram images enhancement using a contrast-modulated nonlinear diffusion. The original nonlinear diffusion is gradient driven, which leads into much dependence on the accurate estimation of the edge. However, it is very difficult to get the accurate estimation of edges for X-ray angiogram images, which are characterized with complex background. So it is necessary to do some improvements to this model. By designing a new concept of contrast space according to the characteristics of these images, we change the original nonlinear diffusion into contrast-modulated nonlinear diffusion. Compared with the traditional method, this new approach is proved to have a better performance of enhancement

关键词： X-ray imaging Anisotropic magnetoresistance image processing image analysis control engineering education intelligent control image enhancement Biomedical imaging Medical diagnostic imaging Chemicals

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Detection of small target in infrared image sequences using attention mechanism

Detection of small target in infrared image sequences using ...

引用

Systems and control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Xun Yao Dehua Li Xianbin Sun Institute of pattern recognition and artificial intelligence Key Laboratory of Education Ministry for Image processing & Intelligence Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780393953

The requirement of algorithm for detecting small target in infrared image sequences is not only a high rate to identify the true target and a low false alarm rate, but also the computation efficiency. By employing the selective attention mechanism, biological visual system can interpret a complex incoming image in real time with limited hardware resources. Much evidence has suggested that the biological visual system processes information in a serial strategy which rapidly selects a small relevant region in scene for further complex and time consuming analysis. In this paper, a two component computation scheme is proposed to detect dim targets in infrared image sequences. In the first process stage, an efficient method is applied to extract the potential targets which are further identified in second phase, the true targets are detected, and the spurious objects are rejected. The attention-based approach reduces the computation complexity, while the other performance aspects are not traded off. Experimental results indicate that the proposed extraction and recognition modules exhibit excellent performance of detecting small target in infrared image sequences, especially in process speed aspect.

关键词： feature extraction image sequences infrared imaging biological visual system computation complexity computation efficiency false alarm rate feature extraction image recognition modules infrared image sequences selective attention mechanism small target d

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Dim Small Targets Detection Based on Dualband Infrared image Fusion

Dim Small Targets Detection Based on Dualband Infrared Image...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yu-Qiu Sun Jin-Wen Tian Jian Liu State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recongnition and Artificial Intelliegence Huazhong University of Science and Technology Wuhan China School of Information and Mathematics Yangtze University Jingzhou China State Education Commission Key Laboratory for Image Processing and Intelligent Control Department of Electronic Information and Enginery Huazhong University of Science and Technology Wuhan China

To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave and long-wave infrared images and detect targets. Firstly, the source images are decomposed by wavelet transformation. In usual, targets in infrared images are man-made, and their fractal dimension is different comparing with natural background. In wavelet transformation domain high-frequency part, we calculate local fractal dimension and set up fusion rule to merge corresponding sub-images of two matching source images. In low-frequency, we extract local maximum gray level to fuse them. Then reconstruct image by wavelet inverse transformation and obtain fused result image. In fusion results, the contrast between targets and background has obvious changes. And targets can be detected using contrast thresholding. The experimental results show that the method using fractal dimension to fuse dualband infrared images, and then detect targets is superior to use mid-wave or long -wave infrared images detect targets alone.

关键词： Object detection Dual band Infrared imaging Infrared detectors image fusion Fuses Fractals Concrete Shape Wavelet domain

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3D TRUS image Segmentation in Prostate Brachytherapy

3D TRUS Image Segmentation in Prostate Brachytherapy

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mingyue Ding L. Gardi Zhouping Wei A. Fenster Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Imaging Research Laboratories Robarts Research Institute London ONT Canada

Brachytherapy is a minimally invasive interventional surgery used to treat prostate cancer. It is composed of three steps: dose pre-planning, implantation of radioactive seeds, and dose post-planning. In these procedures, it is crucial to determine the positions of needles and seeds, measure the volume of the prostate gland. Three-dimensional transrectal ultrasound (TRUS) imaging has been demonstrated to be a useful technique to perform such tasks. Compared to CT, MRI or X-ray imaging, US image suffers from low contrast, image speckle and shadows, making it challenging for segmentation of needles, the prostates and seeds in the 3D TRUS images. In this paper, we reviewed 3D TRUS image segmentation methods used in prostate brachytherapy including the segmentations of the needles, the prostate, as well as the seeds. Furthermore, some experimental results with agar phantom, turkey and chicken phantom, as well as the patient data are reported

关键词： image segmentation Brachytherapy Needles Minimally invasive surgery Ultrasonic imaging Imaging phantoms Oncological surgery Prostate cancer Position measurement Volume measurement

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Dualband Infrared images Target Detection Algorithm Based on Multiscale Morphology

Dualband Infrared Images Target Detection Algorithm Based on...

引用

2006 8th International Conference on Signal processing

作者： Yuqiu Sun Jinwen Tian Jian Liu School of Information and Mathematics Yangtze University State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and Technology

According to the features of mid-wave and long-wave infrared images,they are decomposed into morphology pyramid respectively based on the new multiscale mathematical morphology filters proposed in the *** features such as local maximum gray level and average gradient strength of every image are extracted at each level of morphology *** dualband infrared images based on fusion rule put forward in the paper,and then reconstruct original image and detect target using contrast threshold *** experiment results show that dualband infrared images target detection algorithm based on multiscale morphology algorithm is better than use mid-wave or long-wave infrared images detect targets alone.

关键词： image Dualband Infrared images Target Detection Algorithm Based on Multiscale Morphology Target

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A Combining Method for Tumors Detection from Near-infrared Breast Imaging

A Combining Method for Tumors Detection from Near-infrared B...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Zhicheng Wang Jian Liu Jinwen Tian Zeping Xie State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. & Tech. Wuhan 430074 China Key Lab. for Image Process. & Intelligent Control State Educ. Comm. School of Life Science & Technology Huazhong University of Science and Technology Wuhan China

This paper introduces the new qualitative and quantitative methods, which can diagnose breast tumors. Qualitative methods include blood vessel display inside and outside of pathological changes part of breast, display of equivalent pixel curves at the part of pathological changes and display of breast tumor image edge. Accordingly, three feature extraction operators are proposed, i.e. the combination operators of anisotropic gradient and smoothing operator, an improved Sobel operator and an edge sharpening operator. Furthermore, quantitative diagnostic approaches are discussed based on blood and oxygen contents according to abundant clinical data and pathological mechanism of breast tumors. The results of clinic show that the methods of combining qualitative and quantitative diagnose are effective for breast tumor images, especially for early and potential breast cancer

关键词： Breast tumors Displays Pathology Blood vessels Biomedical imaging Pixel Feature extraction Anisotropic magnetoresistance Smoothing methods Breast cancer

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ROAD CENTRAL CONTOUR EXTRACTION FROM HIGH RESOLUTION SATELLITE image USING TENSOR VOTING FRAMEWORK

ROAD CENTRAL CONTOUR EXTRACTION FROM HIGH RESOLUTION SATELLI...

引用

2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： SHENG ZHENG JIAN LIU WEN-ZHONG SHI GUANG-XI ZHU Electronic & Information Engineering Dept. Huazhong University of Science and Technology Wuhan 430 State Education Commission Key Laboratory for Image Processing and Intelligent Control Inst.for Pat Advanced Research Center for Spatial Information Technology Department of Land Surveying and Geo-In Chinese Key lab of optic & electronics Huazhong University of Science and Technology Wuhan 430074

In this paper, a unique road contour extraction approach from high resolution satellite image is proposed, in which the road contour was extracted in two steps. Firstly, support vector machines (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. The identified road group images are the discrete and irregularly distributed sampled points, and they are an uncompleted data set for the road. Secondly, the road contour was extracted from the road group images using the tensor voting framework, since the tensor voting technique is superior to the traditional methods in extracting the geometrical structure from the uncompleted data set. The experimental results on the high resolution satellite image demonstrate that the proposed approach worked well with images comprised by both rural and urban area features.

关键词： Tensor voting framework Road central line extraction High-resolution satellite image

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