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检索条件"任意字段=5th International Symposium on Multispectral Image Processing and Pattern Recognition"

共 1596 条记录，以下是71-80 订阅

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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...

引用

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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Multiscale kernel method for image matching

Multiscale kernel method for image matching

引用

5th international symposium on multispectral image processing and pattern recognition

作者： Hui Cheng Jing Zhou Ma, Shian Shen, Dajiang Tian, Jinwen Jianghan Univ Sch Math & Comp Sci Wuhan 430056 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

In this paper, a new multiscale kernel function model is proposed, according to kernel function nature and the wavelet frame theory. thus a new image support feature is defined based on the multiscale wavelet kernel regression for the purpose of improving image matching algorithm. the comprehensive the feature match and grey correlation method, a novel image fast matching algorithm is proposed based on support feature points. the partial Hausdorff distance is adopted as a similarity measure combined with the wavelet decomposition iteration to search the fine strategy, in the wavelet domain. According to support feature points the position in original image, extracts corresponding the grey value, and achieves the correlation matching. the matched data are compressed effectively because support feature points are sparse. Experimental results demonstrate that the proposed algorithm is robust, fast and can achieve matching accurately.

关键词： kernel functions image matching image feature support vector regression wavelet

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Method of passive ranging from infrared image sequence based on equivalent area

Method of passive ranging from infrared image sequence based...

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5th international symposium on multispectral image processing and pattern recognition

作者： Yang, Weiping Shen, Zhenkang Changsniv Def Technol Changsha ATR Key Lab Hunan 410073 Peoples R China

ISBN: (纸本)9780819469502

the information of range between missile and targets is important not only to missile controlling component, but also to automatic target recognition, so studying the technique of passive ranging from infrared images has important theoretic and practical meanings. Here we tried to get the range between guided missile and target and help to identify targets or dodge a hit. the issue of distance between missile and target is currently a hot and difficult research content. As all know, infrared imaging detector can not range so that it restricts the functions of the guided information processing system based on infrared images. In order to break through the technical puzzle, we investigated the principle of the infrared imaging, after analysing the imaging geometric relationship between the guided missile and the target, we brought forward the method of passive ranging based on equivalent area and provided mathematical analytic formulas. Validating Experiments demonstrate that the presented method has good effect, the lowest relative error can reach 10% in some circumstances.

关键词： infrared image sequences passive ranging

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Road information extraction from IKONOS imagery based on clustering analysis and mathematical morphology

Road information extraction from IKONOS imagery based on clu...

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5th international symposium on multispectral image processing and pattern recognition

作者： Song, Yang Wan, Youchuan Shen, Shaohong Wang, Hong Hubei Univ Econ Sch Comp Beijing 430205 Peoples R China Wuhan Univ Sch Remote Sensing Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9780819469502

In this paper, we present an approach based on clustering analysis and mathematical morphology to extract road information from IKONOS imagery. this road information extraction approach includes several key modules: Texture analysis based on the multi-band image to obtain two new features of "MLen/MWid" to improve the road clustering analysis;In order to optimize the primal binary imagery of road object area resulting from clustering Process, a texture analysis defined on binary imagery-"BATS" is presented, which ulteriorly expel the non-road pixels from the road area binary imagery;Furthermore, we carry out the process to extract road centerline network from the binary imagery of road object area based on mathematical morphology, through the process, several other methods, such as connectivity analysis, raster to vector transform, etc, are integrated.

关键词： remote sensing high-resolution imagery pattern recognition road information images fusion texture analysis clustering fuzzy logic mathematical morphology

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Detection of space image target based on improved fractal technique

Detection of space image target based on improved fractal te...

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5th international symposium on multispectral image processing and pattern recognition

作者： Tian, Balin Yuan, Jianping Tian, Jing Liang, Huaqiang Northwestern Polytech Univ Coll Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Coll Astronaut Xian 710072 Peoples R China Xian Inst Commun Xian AH-710106 Peoples R China

ISBN: (纸本)9780819469502

In the space surveillance applications, recognition of image targets is very important, one of the most difficult problems in image processing is detecting objects in an image. Target detection is a key step for the applications of space image target recognition. An improved method presented in this paper is designed to detect man-made objects in digital images representing in natural environments. this method is based on Fractal theory. According to the fractal feature of man-made object in space, the object can be partitioned off background based on the difference of intrinsic fractal features between them, where the value of fractal dimension (FD) at the objects' edge is much larger than that in other area. the validity of the proposed method is examined by processing and analyzing images of space target at the end of the paper. In detection of targets for real images of Shenzhou-VI and launch vehicle ChangZheng(CZ), the algorithm has a successful outcome. It will provide great efficiency and speed in detection of space image target, which could be also used for further image feature extraction and image segmentation.

关键词： man-made object target detection fractal fractal dimension (FD) box-counting dimension

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Hyperspectral data classification using image fusion based on curvelet transform

Hyperspectral data classification using image fusion based o...

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5th international symposium on multispectral image processing and pattern recognition

作者： Sun, Airong Tan, Yihua Wuhan Inst Technol Sch Engn & Comp Sci Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sate Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469519

A new supervised classifier based on image fusion of hyperspectral data is proposed. the technique first selects the suitable bands as the candidates for fusion. then, the bands based on curvelet transform are fused into several components. the fused hyperspectral components as the extracted features are fed into the supervised classifier based on Gaussian Mixture Model. After the estimation of the GMM with Expectation Maximization, the pixels are classified based on the Bayesian decision rule. One requirement of the technique is that the training samples should be provided from the hyperspectral data to be analyzed. the main merits of the new method contain tow folds. One is the novel feature extraction based on curvelet transform which fully makes use of the spectral properties of the hyperspectral data. the other one is the low computing complexity by reducing the data dimension significantly. Experimental result on the real hyperspectral data demonstrate that the proposed technique is practically useful and posses encouraging advantages.

关键词： hyperspectral data curvelet transform fusion classification

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Detail preserving robust anisotropic diffusion for image enhancement and noise reduction

Detail preserving robust anisotropic diffusion for image enh...

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5th international symposium on multispectral image processing and pattern recognition

作者： Wang, Yi Niu, Ruiqing Zhang, Liangpei Li, Pingxiang China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

ISBN: (纸本)9780819469519

Recently, many researchers have shown interests in anisotropic diffusion methods in image processing. there are two key problems on image restoration techniques based on anisotropic diffusion: one is to build stable diffusion coefficients;the other is to select the optimal stopping time for iterative diffusion process. In this paper, a time-dependent robust anisotropic diffusion method is proposed. the new method is a robust anisotropic diffusion with incorporated time dependent cooling process, and the gradient threshold is the monotonic decreasing function of the time. thus, the parameters settings and the estimate of the optimal stopping time can be easily resolved. Meanwhile, the proposed method can lessen the over-smooth in image edge features. In order to extent the applicability of the proposed method, we extent the model to the vector-valued form and present time-dependent multispectral robust anisotropic diffusion. Experimental results, performed on several gray images, color images and multispectral remote sensed images, have shown that the time-dependent robust anisotropic diffusion methods can effectively smooth out noise while preserving edge features.

关键词： detail preserving robust anisotropic diffusion parameter tuning

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image registration method based on evolutionary modeling with constraints

Image registration method based on evolutionary modeling wit...

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5th international symposium on multispectral image processing and pattern recognition

作者： Wang, Zongyue Ma, Hongchao Zhang, Jianwei Huang, Zhangcan Wu, Jianwei Xu, Honggen Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Hubei Peoples R China Jimei Univ Comp Engn Coll Xiamen 3610219 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China

ISBN: (纸本)9780819469502

Considering the deficiency of mapping model in traditional image registration, a new image registration method based on evolutionary modeling is proposed in this paper. Multi Expression Programming has been used as modeling tool to build mapping model. To avoid over fitting and improve actual effective, constraints of the mapping function's slope and curvature were added during modeling process. SAR image and optical image rectifying experiment is given in the last. the experiment result indicated that the evolutionary model has high precision for image registration. this method is fit for image registration.

关键词： image registration evolutionary modeling Multi Expression Programming (MEP)

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Wavelet-based SAR images despeckling using Joint Hidden Markov Model

Wavelet-based SAR images despeckling using Joint Hidden Mark...

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5th international symposium on multispectral image processing and pattern recognition

作者： Li, Qiaoliang Wang, Guoyou Liu, Jianguo Chen, Shaobo Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469519

In the past few years, wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. the hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the deficiency for taking account of intrascale correlations that exist among neighboring wavelet coefficients. In this paper, We propose to develop a joint hidden Markov model by fusing the wavelet Bayesian denoising technique with an image regularization procedure based on HMT and Markov random field (MRF). the Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model. the noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. It is shown that the joint method outperforms lee filter and standard HMT techniques in terms of the integrative measure of the equivalent number of looks (ENL) and Pratt's figure of merit(FOM), especially when dealing with speckle noise in large variance.

关键词： Bayesian estimation Markov random field speckle Joint Markov Model wavelet denoising

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Moving point target trajectory detection based on dual-Hough transform

Moving point target trajectory detection based on dual-Hough...

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5th international symposium on multispectral image processing and pattern recognition

作者： Hu, Jing Zhang, Tian-Xu Inst Pattern Recognit Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Educ Minist Image Proc & Intelligent Cont Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

this paper deals with research on detecting moving point target trajectory in image sequence. A novel method is presents for this purpose, which combines two 2-dimension Hough transforms to suppress noise points and to detect trajectory points in time order. the first Hough transform has an accumulators array using a restricted voting process and a set of straight lines are found in the image plane. A new T-L parameter space is proposed which is derived from these straight lines. In the second transform, collinear points are mapped into T-L space and it is easy to find the direction of motion. Experimental results show that our method can effectively extract moving point target trajectory accurately in a limited observing time especially scanning images from large numbers of noise points while search region is much larger than target movability.

关键词： moving point target (MPT) trajectory detection dual-Hough transform (DHT) X-Y-T observation space T-L parameter space

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