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检索条件"任意字段=MIPPR 2011: Pattern Recognition and Computer Vision"

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Matching SAR image to optical image using modified hausdorff distance and genetic algorithms

Matching SAR image to optical image using modified hausdorff...

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Conference on pattern recognition and computer vision

作者： Mao, Haicen Yu, Qiuze Zhang, Tianxu Huazhong Inst Electroopt Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Key Lab Educ Ministry Image Processing & Intellig Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

A novel algorithm for matching synthetic aperture radar (SAR) image to Optical image based on lineal feature using Hausdorff distance combined with genetic algorithm is proposed in this paper. A new method is presented to extract lineal feature from low signal to noise ratio (SNR) SAR image. Based on the edge image from SAR and Optical image, modified Hausdorff distance is adopted as a similarity measure because it is insensitive to noise. Genetic algorithm is used as searching strategy to achieve high computation speed for its inertial parallel. Experimental results using real SAR and Optical images demonstrate that the algorithm is robust, fast and can achieve high matching accuracy.

关键词： synthetic aperture radar (SAR) image image matching hausdorff distance genetic algorithm signal to noise ratio (SNTR)

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Self calibration of camera with non-linear imaging model

Self calibration of camera with non-linear imaging model

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Conference on pattern recognition and computer vision

作者： Hou, Wenguang Shang, Tao Ding, Mingyue Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Image Proc & Intelligent Control Key Lab Educ Minist China 1037 Luoyu Rd Wuhan 430074 Peoples R China Wuhan Univ Sch Urban Design Wuhan 430072 Peoples R China

ISBN: (纸本)9780819469526

Being put forward by the researchers in computer vision, self calibration commonly deals with camera with linear model. Since the distortion is practically existed especially for ordinary camera, the result of calibration can't meet the demand of vision measurement with high accuracy regardless of the distortion. Being obedience to systematism mainly, the distortion is the target function of distortion coefficient, principal point, principal distance ratio and skew factor etc. So there exists a group of parameters including of distortion coefficient, principal point, principal distance ratio and skew factor and fundamental matrix which make homologous point meets epipolar restriction theoretically. Accordingly, the paper advances the way titled self calibration of camera with non-linear imaging model which is on basis of the Kruppa equation. In calculating the fundamental matrix, we can obtain interior elements except principal distance by taking into account distortion correction about image coordinate. Then the principal distance can be obtained by using Kruppa equation. This way only need some homologous points between two images, not need any known information about objects. Lots of experiments have proven its correctness and reliability.

关键词： self calibration Kruppa equation non-linear imaging model fundamental matrix camera calibration

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Research on multi-class classification of Support Vector Data Description

Research on multi-class classification of Support Vector Dat...

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Conference on pattern recognition and computer vision

作者： Shen, Minghua Xiao, Huaitie Fu, Qiang Natl Univ Def & Technol Sch Elect Sci & Engn Changsha 410073 Peoples R China

ISBN: (纸本)9780819469526

Support Vector Data Description (SVDD) is a one-class classification method developed in recent years. It has been used in many fields because of its good performance and high executive efficiency when there are only one-class training samples. It has been proven that SVDD has less support vector numbers, less optimization time and faster testing speed than those of two-class classifier such as SVM. At present, researches and acquirable literatures about SVDD multi-class classification are little, which restricts the SVDD application. One SVDD multi-class classification algorithm is proposed in the paper. Based on minimum distance classification rule, the misclassification in multi-class classification is well solved and by applying the threshold strategy the rejection in multi-class classification is greatly alleviated. Finally, by classifying range profiles of three targets, the effect of kernel function parameter and SNR on the proposed algorithm is investigated and the effectiveness of the algorithm is testified by quantities of experiments.

关键词： Support Vector Data Description kernel function parameter pattern recognition multi-class classification

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From primal sketch to 2.1D sketch with contour reorganization

From primal sketch to 2.1D sketch with contour reorganizatio...

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Conference on pattern recognition and computer vision

作者： Gao, Ru-xin Sang, Nong Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

This paper proposed a model for 2.1D sketch based on primalsketch [6,7] and a corresponding algorithm based on SWC(Swendsen-Wang Cut)[1,2]. Given a primal sketch graph, all boundaries' color and curvature information can be gotten from the dictionary of image primitives of the primal sketch. Suppose all curves in the sketch graph can be combined into closed contours and then contour can be combined into regions. Calculating boundaries similarity energy of two regions by comparing the comparability using the color and curvature information of boundaries, then the energy is regarded as the proposal of SWC. All regions are regarded as nodes of graph space, then the 2.1D sketch problem can be looked as graph partition problem. In each step, all curves will be reorganized into contours and then contours generate regions. Some experiment results based on above model and algorithm are given in the paper.

关键词： layer representation 2.1D sketch Bayesian inference contour completion mixed Markov field Swendsen-Wang cut MCMC

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Performance research of Gaussian function weighted fuzzy C-means algorithm

Performance research of Gaussian function weighted fuzzy C-m...

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Conference on pattern recognition and computer vision

作者： Liu, Xiaofang Li, Xiaowen Yang, Chun He, Binbin Zhang, Ying Univ Elect Sci & Technol China Inst Geospatial Informat Sci & Technol Chengdu 610054 Peoples R China Sichuan Univ Dept Comp Sci & Technol Zigong 643000 Peoples R China Sichuan Univ Dept Engn Management Zigong 643000 Peoples R China

ISBN: (纸本)9780819469526

Fuzzy C-Means (FCM) algorithm is a fuzzy pattern recognition method. Clustering precision of the algorithm is affected by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian function Weighted Fuzzy C-Means (WFCM) algorithm is proposed, which the weighted function is produced by a Gaussian function calculating dot density of each sample. To certain extent, the WFCM algorithm has not only overcome the limitation of equal partition trend in fuzzy Cmeans algorithm, but also been favorable convergence and stability. The calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFCM algorithm, the classification performance of the WFCM algorithm is further enhanced and the convergent speed of objective function is further accelerated.

关键词： fuzzy C-means algorithm weighted fuzzy C-means algorithm partially supervised and weighted fuzzy C-means algorithm Gaussian function pattern recognition fuzzy clustering

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Novel framework for producing multi-scale and multi-viewpoint images based on remote sensing stereopair - art. no. 67881G

Novel framework for producing multi-scale and multi-viewpoin...

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Conference on pattern recognition and computer vision

作者： Tan, Ying Cao, Zhiguo Li, Yukun Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

Automatic target recognition(ATR) is the key of the image guidance technology, yet it is difficult to recognize the target by merely depending on the real-time image acquired by flying vehicle cameras, moreover, the task of recognizing the target from the real-time images by the vehicle-carrying image processing system is a hard work itself The main trend of the ATR nowadays is to make utilization of the images produced by high-resolution remote sensing satellite to retrieve the front elevation of the interested region before hand. These front elevations are loaded upon the flying vehicles and are matched with the real-time images acquired by vehicle-carrying cameras to recognize the interested target. Obviously, the key step of this method is to recover the 3D information from 21) images. This paper proposed a framework to produce multi-scale and multi-viewpoint projection images based on remote sensing satellite stereopair by means of photogrammetry and computer vision. First we proposed a algorithm for reconstructing the 3D structure of the target by digital photogrammetric techniques and establishing the 3D model of the target using the OpenGL visualization toolkit. Then the conversion relationship between the world coordinate system and the simulation space coordinate system is provided to produce the front elevation in the simulation space.

关键词： 3D reconstruction multi-scale multi-viewpoint front elevation gauss plane gauss coordinates system simulation space

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vision-based 3D registration of outdoor AR system

Vision-based 3D registration of outdoor AR system

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mippr 2007: pattern recognition and computer vision

作者： Xueling, Wu Qingyun, Du Fu, Ren Tao, Liu School of Resource and Environmental Science Wuhan University 430079 Wuhan China Institute of Geophysics and Geomatics China University of Geosciences 430074 Wuhan China

ISBN: (纸本)9780819469526

One of the key technologies of Outdoor AR is the real-time 3D registration of objects in the real world. The paper puts forward a new hardware registration method which not only borrows the ideas of identification point registration, but improves it to realize tracking registration in video-based outdoor AR, which uses see-through head mounted display (STHMD) loaded on outdoor AR system for showing the result of registration, and employs one color CCD camera capturing video to obtain the world coordinate of scene border. Furthermore, the paper utilizes 3D electronic compass and GPS attached on user's body to calculate transition matrix from the world coordinate system to the camera coordinate system. Then, the transition matrix from the virtual coordinate system to the image plane can be calculated out and 3D virtual object generated by computer model is added into the STHMD as a whole. Synthetically, video-based registration offers a superior approach to 3D registration of dynamic object. Finally, the paper provides the implementation process and designs a test. By the case study, the new method significantly simplifies the registration system and algorithm, and coordination errors are eliminated. The algorithm requires little computation and can be easily realized in real time without delay. Compared with the several existing registration methods, it is significantly improved.

关键词： Real time systems

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High resolution remote sensing image classification with multiple classifiers based on mixed combining rule - art. no. 67881J

High resolution remote sensing image classification with mul...

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Conference on pattern recognition and computer vision

作者： Chen, Zhong Liu, Jianguo Wang, Guoyou Li, Qiaoliang Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

The development of the remote sensing technology makes us obtain very abundant information of nature, especially with the appearance of high resolution remote sensing image it extends the visual field of the nature. High-resolution satellite images such as Quickbird and IKONOS have been applied into many fields. But the challenge that faces us is how to make use of the data effectively and obtain more useful information through some processing. Because in the target recognition, the mutual-complementarity among the different results obtained by the different classifier making using of the same features usually is very strong and high resolution remote sensing data have a lot of characteristics such as spectral, texture and context and so on compared to the other lower resolution remote sensing data, the Multiple Classifiers making use of multi-characteristic was proposed to improve the high resolution remote sensing image classification accuracy in this paper. The experiments show that the approach can obtain higher classification accuracy and better classification result than single classifier.

关键词： multiple classifiers multi-characteristic high resolution remote sensing image

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Novel image matching confidence fusion evaluation algorithm based on Support Vector Machine - art. no. 67881D

Novel image matching confidence fusion evaluation algorithm ...

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Conference on pattern recognition and computer vision

作者： Zou, Lamei Cao, Zhiguo Zhang, Tianxu Huazhong Univ Sci & Technol State Key Lab Multispectral Informat Proc Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

Confidence evaluation is an important technique in image matching process. This paper proposes a confidence level evaluation method for image matching result based on support vector machine (SVM). We divide the matching result into two different types: the correct result and the wrong result. So we translate the match result's confidence evaluation problem into the matching result's classification. This paper firstly provides a method of how to prepare the character parameters which can accurately reflect the matching performance. And then the SVM based on Gaussian kernel is used as a classifier to classify the match result and discriminate the match result's type. The experiments show that this method is effective. Compared with the Dempster-Shafer (D-S) evidence reasoning fusion method it has much higher accuracy.

关键词： image matching confidence evaluation support vector machine Dempster-Shafer evidence reasoning

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Stick-guided lateral inhibition for enhancement of low-contrast image

Stick-guided lateral inhibition for enhancement of low-contr...

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Conference on pattern recognition and computer vision

作者： Tu, Shengxian Wu, Yilun Lu, Xuesong Huo, Hong Fang, Tao Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Inst Image Processing & Pattern Recognit Shanghai Peoples R China

ISBN: (纸本)9780819469526

The inhibitory interaction has long been observed in the lateral eye of the Limulus and been integrated into mechanism of enhancing contrast. When applying to the enhancement of low-contrast image for segmenting interested objects, the original lateral inhibition model will simultaneously amplify noises while enhancing edges contrast, This paper presents a new lateral inhibition model, which is called Stick-Guided Lateral Inhibition, for enhancement of low-contrast image so that week edges may exert a stronger force to catch the boundary of targets in the latter segmentation. First, the guided inhibition term is introduced as a general framework for improving the performance of lateral inhibition models in the presence of noises. Then, by using asymmetric sticks to guide the inhibiting process, we are able to accentuate the intensity gradients of image-edges and contours while suppressing the amplification of noises. Experiments on synthetic images and remote sensor images show that our model significantly enhances low-contrast images and improves the performance of latter segmentation.

关键词： asymmetric sticks enhancement lateral inhibition low-contrast

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