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检索条件"任意字段=MIPPR 2007: Multispectral Image Processing"

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Point matching of two images under large different resolution

Point matching of two images under large different resolutio...

引用

mippr 2007: Automatic Target Recognition and image Analysis;and multispectral image Acquisition

作者： Ruan, Lei Hu, Maolin Lab. of Intelligent Computing and Signal Processing AnHui University Hefei AnHui 230039 China Key Laboratory of Wireless Sensor Network and Communication Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

ISBN: (纸本)9780819469502

image matching is the first step in almost any 3D computer vision task, and hence has received extensive attention. In this paper, the problem is addressed from a novel perspective, which is different from the classic stereo matching paradigm. Two images with different resolutions, that is high resolution versus low resolution are matched. Since the high resolution image only corresponds to a small region of the low resolution one, the matching task therefore consists in finding a small region in the low resolution image that can be assigned to the whole high resolution image under the plane similarity transformation, which can be defined by the local area correlation coefficient to match the interest points and rectified by similarity transform. Experiment shows that our matching algorithm can be used for scale changing up to a factor of 6. And it is successful to deal with the point matching between two images under large scale.

关键词： image matching

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PCA based forward-looking infrared airport recognition combining intensity and shape feature

PCA based forward-looking infrared airport recognition combi...

引用

5th International Symposium on multispectral image processing and Pattern Recognition

作者： Liu, Wei Tian, Jinwen Chen, Xinwu Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multispectral Informat Processing T Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

In this paper, a novel method based on PCA with shape and intensity information is proposed for infrared forward-looking airport recognition. Here, PCA is used to perform feature transformation and airport recognition. It maps an input image into a low-dimensional feature space in order to make the mapped features linearly separable. And the input image of conventional method only uses intensity information. The proposed method not only considers the intensity but also adopts shape-mask to emphasize the important object area information. The novel method is evaluated based on the sequence of infrared forward-looking airport images by using different airport recognition methods such as BP networking and SVM. The experiment's results have been compared based on percentage of correct classification, computation complexity and amount of training data, which show that this new method is superior to other recognition approach on computation complexity under almost the same recognition accuracy.

关键词： PCA forward-looking infrared image shape mask cluster segmentation

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PCA-based visualization of terahertz time-domain spectroscopy image

PCA-based visualization of terahertz time-domain spectroscop...

引用

5th International Symposium on multispectral image processing and Pattern Recognition

作者： Pei, Jihong Hu, Yong Xie, Weixin Shenzhen Univ Intelligent Informat Inst Shenzhen 518060 Peoples R China

ISBN: (纸本)9780819469519

A novel visualization method of terahertz time-domain spectroscopy (THz-TDS) image is presented, which is based on principal component analysis (PCA) technique. The proposed method include three processing steps: firstly, the THz-TDS image is preprocessed using a spatial vector filtering technique to denoise. Secondly, the THz-TDS image is transformed from spatio-temporal domain to spatio-spectral domain, and the transformed image can be viewed as a multispectral image whose spectral dimensionality D is equal to the sampled number of THz-TDS pulse at each pixel. Thirdly, each of spectrum vector at a pixel is viewed as a point in D dimensional space, the covariance matrix of pixels can be computed, and then three eigenvectors corresponding to the first 3 largest eigenvalues are found by PCA technique. the THz-TDS image is projected along these three eigenvectors. By normalizing these 3 principal component images and mapping them into the RGB space, we can get a synthetic color image as a visualization result of the THz-TDS image. Due to vector-based dimensionality reduction, the proposed method can provide more visual information of the THz-TDS image than scalar-based visualization techniques. Finally, experimental results are provided to demonstrate the performance of the proposed method.

关键词： terahertz time-domain spectroscopy THz-TDS image PCA visualization dimensionality reduction

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Region based image fusion using SVM

Region based image fusion using SVM

引用

5th International Symposium on multispectral image processing and Pattern Recognition

作者： Liu, Yang Cheng, Jian Lu, Hanqing Chinese Acad Sci Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9780819469519

This paper presents a novel fusion approach using PCA merger based on multiscale decomposition (MSD), combined with region segmentation and support vector machine (SVM), the result is a high spatial resolution multispectral image from a high resolution panchromatic (Pan) image and low resolution multispectral (Ms) images. Principal components analysis (PCA) fusion technique is one of typical fusion methods, and PCA merger based on MSD had been proposed which can obtain better performance. As we know that, in pixel fusion level, the original images are fused as internal region regardless of the contents of images, but in this paper, we perform region segmentation after MSD, because the homogeneous regions have similar features such as color, texture and intensity. Traditionally, various fusion rules can be applied after MSD according to different conditions, however, the crucial problem is which fusion rule should be adopted under given condition, hence we use the SVM to combine the most fusion rules so that can avoid some drawbacks using single fusion rule. To validate our approach, we compare it with several typical fusion approaches, and the best result is obtained using our approach.

关键词： image fusion multiscale decomposition SVM PCA

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Application of SGNN-based method in image segmentation

Application of SGNN-based method in image segmentation

引用

mippr 2007: Automatic Target Recognition and image Analysis;and multispectral image Acquisition

作者： Li, Lu Jiang, Hong Ren, Zhang Zhang, Yong-Fei School of Automation Science and Electrical Engineering Beihang University Beijing 100083 China

ISBN: (纸本)9780819469502

In this paper, a SGNN (Self-Generating Neural Network)-based method is applied to image segmentation, which is implemented automatically by autonomously clustering the pixels according to their gray values. The optimization of SGNN is studied to further improve the accuracy and robustness, as well as to reduce the computational complexity of the segmentation. The experimental results show that the optimized SGNN gets better segmentation results and outperforms the existing methods for its distinguished advantages of perfect segmentation without any manual intervention, high self-learning capacity, less computational complexity, robustness to noise, etc. What's more, the experimental results suggest that the proposed method can be widely used in segmentation of all typical images, such as IR (Infrared) images, visible images, X-ray images, and MR (Magnetic Resonance) images.

关键词： image segmentation

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Combining color and texture features for image retrieval

Combining color and texture features for image retrieval

引用

5th International Symposium on multispectral image processing and Pattern Recognition

作者： Wang, Guiting Tian, Baobao Jiao, Licheng Xidian Univ Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819469502

In this paper, a new method named BQCGW (block-based method of combining quantized colors and Gabor wavelet features) is proposed for image retrieval. HSV color space, in which measured color differences are proportional to the human perception of such differences, is quantized into 67 kinds of representative colors. We also propose the use of Gabor wavelet features for texture analysis. images in the database are divided into nine blocks before extracting color and texture features. Experiment results show that our method is feasible and valid.

关键词： content-based image retrieval (CBIR) HSV quantized colors Gabor wavelet

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Urban road network extraction from SAR image

Urban road network extraction from SAR image

引用

mippr 2007: Remote Sensing and GIS Data processing and Applications;and Innovative multispectral Technology and Applications

作者： Cheng, Pengxue Hu, Maolin Lab. of Intelligent Computing and Signal Processing Anhui University Hefei Anhui 230039 China Key Laboratory of Wireless Sensor Network and Communication Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

ISBN: (纸本)9780819469540

In this paper, a technique for the extraction of roads in a high resolution synthetic aperture radar (SAR) image is presented. And a three-step method is developed for the extraction of road network from space borne SAR image: the process of the feature points, road candidate detection and connection. Roads in a high resolution SAR image can be modeled as a homogeneous dark area bounded by two parallel boundaries. Dark areas, which represent the candidate positions for roads, are extracted from the image by a Gaussian probability iteration segmentation. Possible road candidates are further processed using the morphological operators. And the roads are accurately detected by Hough Transform, and the extraction of lines is achieved by searching the peak values in Hough Space. In this process, to detect roads more accurately, post-processing, including noisy dark regions removal and false roads removal is performed. At last, Road candidate connection is carried out hierarchically according to road established models. Finally, the main road network is established from the SAR image successfully. As an example, using the ERS-2SAR image data, automatic detection of main road network in Shanghai Pudong area is presented.

关键词： Feature extraction

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Improved algorithm for image decomposition based on bidimensional emd

Improved algorithm for image decomposition based on bidimens...

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5th International Symposium on multispectral image processing and Pattern Recognition

作者： He, Jingbo Peng, Fuyuan Huazhong Univ Sci & Technol Inst Elect & Informat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

According to the system intrinsic quality of self-comparability and the empirical mode decomposition algorithm of completeness and stability, an improvement algorithm for EMD image decomposition is presented. It is integrity, fast and effective. Some aspects were improved that bidimensional interpolation methods and end conditions of getting intrinsic mode image. Three questions of general algorithm for EMD image decomposition were solved. First, the algorithm for image decomposition was slow;Second, some points were not contained because of delaunay triangulation;Third, the end condition of algorithm was not accuracy. Experiments were made by Matlab and the validity of the improvement algorithm was validated.

关键词： delaunay triangulation empirical mode Decomposition(EMD) image decomposition algorithm

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Algorithm for the blind deconvolution of images based on neural networks

Algorithm for the blind deconvolution of images based on neu...

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5th International Symposium on multispectral image processing and Pattern Recognition

作者： Zuo, Boxin Tian, Jinwen Zu, Li Chen, Anhong Huazhong Univ Sci & Technol Inst Recognit & Artificial Intelligence State Educ Commis Lab Image Proc & Intelligence C Wuhan 430074 Peoples R China China Univ Geol Sci Inst Comp Sci Wuhan 430074 Peoples R China Natl Def Key Lab Aerosp Intelligent Control Techn Beijing 100854 Peoples R China

ISBN: (纸本)9780819469519

An iterative blind deconvolution algorithm for degrade image is presented in this paper. The algorithm including two step, namely, the estimation of the point spread function of degrade image and the restoration using estimated point spread function. Two different Hopfield neural networks are built for realize the two steps. An iterative procedure is used to control the restoration process. The simulation results indicate that the method is effective for blind deconvolution with high convergence speed.

关键词： neural networks blind deconvolution PSF image restoration degradation model atmospheric turbulence

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Aerial sequence image mosaic using reduced sift descriptors

Aerial sequence image mosaic using reduced sift descriptors

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5th International Symposium on multispectral image processing and Pattern Recognition

作者： Tuo, Hongya Jing, Zhongliang Zhang, Tinghou Shanghai Jiao Tong Univ Inst Aerosp Sci & Technol Shanghai 200030 Peoples R China

ISBN: (纸本)9780819469502

Sequence image mosaic is an important and effective method to build a large "panoramic" scene which includes two main steps: image registration and intensity blending. In this paper, SIFT feature points are used to match images. SIFT are invariant to rotation, translation and scale changes, but the significant drawback is the high dimensional feature descriptor which lead to the expensive computation. So reduced SIFT descriptors are proposed to increase the speed of image registration. Linear combination methods of the matching points' gray-values are used for intensity blending. The experiments show that our method is useful and has high registration accuracy.

关键词： sequence image mosaic registration SIFT

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