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

共 1594 条记录，以下是81-90 订阅

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Building line feature acquisition based on morphologic filter for infrared images recognition

Building line feature acquisition based on morphologic filte...

引用

MIPPR 2009 - multispectral image Acquisition and processing: 6th international symposium on multispectral image processing and pattern recognition

作者： Li, Zheng Li, Hansong Institution of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478054

Building recognition is an important field in computer vision. Building target line features which represent the target geometry information are stable features in infrared images. In this paper, the stable building line features are acquired by morphology filter algorithm and the number of the correctly obtained line features and the distance similarities of these features are used to measure the probability of the target emerge in the infrared image. From the recognition results, we can see that our algorithm can efficiently recognize this kind of targets. © 2009 Copyright SPIE - the international Society for Optical Engineering.

关键词： Bandpass filters

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A spectral preservation fusion method based on band ratio and weighted combination

A spectral preservation fusion method based on band ratio an...

引用

5th international symposium on multispectral image processing and pattern recognition

作者： Zhu, Wenquan Pang, Xinhua Pan, Yaozhong Jia, Bin Yang, Xiaoqiong Beijing Normal Univ Coll Resources Sci & Technol State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China

ISBN: (纸本)9780819469519

With the availability of multi-sensor data in the field of remote sensing, sensor fusion has emerged as a promising research area. this study presents a simple spectral preservation fusion approach based on band ratio and weighted combination. It injects spatial features into multi-spectral images to improve the spatial information, and adjusts the ratio between the high spatial resolution image and the multi-spectral image with a weight factor to reduce the color distortion. this method is applied to merge SPOT and LANDSAT (TM) images. Visual and statistical analysis prove that the technique presented here is clearly better than the conventional image fusion techniques for preserving the spectral properties with the spatial detail improved synchronously.

关键词： image fusion wavelet remote sensing color distortion IHS PCA

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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...

引用

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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Real time object tracking using adaptive kalman particle filter

Real time object tracking using adaptive kalman particle fil...

引用

5th international symposium on multispectral image processing and pattern recognition

作者： Gao, Lin Tang, Peng Liu, Zhifang Sichuan Univ Image & Graph Inst Dept Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9780819469502

In this paper, a visual object tracking algorithm based on the Kalman particle filter (KPF) is presented. the KPF uses the Kalman filter to generate sophisticated proposal distributions which greatly improving the tracking performance. However, this improvement is at the cost of much extra computation. To accelerate the algorithm, we mend the conventional KPF by adaptively adjusting the number of particles during the resampling step. Moreover, in order to improve the robustness of tracker without increasing the computational load, another two modifications is made: firstly, the covariance matrix of Gaussian noise in the dynamic model is dynamically updated according to the accuracy degree of the prediction. Secondly, the similarity measurement is performed by a scheme that adaptively switches the likelihood models. Experimental results demonstrate the efficiency and accuracy of the proposed algorithm.

关键词： object tracking Kalman particle filter adaptive similarity measurement

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Novel feature extraction method for hyperspectral remote sensing image

Novel feature extraction method for hyperspectral remote sen...

引用

5th international symposium on multispectral image processing and pattern recognition

作者： Liu, Chunhong Zhao, Huijie Beijing Univ Aeronaut & Astronaut Sch Instrument Sci & Optoelect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9780819469519

In order to reduce high dimensions of hyperspectral remote sensing image and concentrate optimal information to reduced bands, this paper proposed a new method of feature extraction. the new method has two steps. the first step is to reduce the high dimensions by selecting high informative and low correlative bands according to the indexes calculated by a smart band selection method. the criterions that SBS method complied are: (1) the selected bands have the most information;(2) the selected bands have the smallest correlation with other bands. the second step is to decompose the selected bands by a novel second generation wavelet, predicting and updating subimages on rectangle and quincunx grids by Neville filters, finally using variance weighting as fusion weight. A 126-band HYMAP hyperspectral data was experimented in order to test the effect of the new method. the results showed classification accuracy is increased by using the novel feature extraction method.

关键词： hyperspectral remote sensing image smart band selection second generation wavelet fusion classification

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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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Robust face tracking algorithm with occlusions

Robust face tracking algorithm with occlusions

引用

5th international symposium on multispectral image processing and pattern recognition

作者： Wang, Zhanqing Fan, Youfu Zhang, Guilin Hu, Ruolan Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China HUST Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China Wuhan Univ Technol Coll Sci Wuhan 430070 Peoples R China

ISBN: (纸本)9780819469502

We propose an adaptive model update mechanism for face tracking based on mean-shift, we employ the Kalman filter to predict a proper original position for mean shift tracking algorithm. To overcome the problem of appearance change, an adaptive modal update is introduced. We classify the occlusion problems into two main cases specified as partial occlusion and complete occlusion according to the number of similar sub blocks between object and candidate. We fuss Kalman predictor into Mean-shift tracker in case of partial occlusion, for case of full occlusion, we divide object and candidate into four parts respectively, according to the previous exact tracking result, we compute the average velocity of the target, and then check the condition for face reappearing, with which we present an efficient target search strategy to deal with full occlusion. Various tracking sequences demonstrate the superior behavior of our tracker and its robustness to appearance changes and occlusions.

关键词： mean shift Kalman filter occlusion face tracking

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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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Multiscale image segmentation based on one class svm and wavelet

Multiscale image segmentation based on one class svm and wav...

引用

5th international symposium on multispectral image processing and pattern recognition

作者： Xiang, HaiLin Sun, Qiang Jiao, LiCheng Xidian Univ Inst Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9780819469502

A supervised multiscale image segmentation method is presented based on one class support vector machine (OCSVM) and wavelet transformation. Wavelet coefficients of training images in the same directions at different scale are organized into tree-type data as training samples for OCSVMs. Likelihood probabilities for observations of segmentation image can be obtained from trained OCSVMs. Maximum likelihood classification is used for image raw segmentation. Bayesian rule is then used for pixel level segmentation by fusing raw segmentation result. In experiments, synthetic mosaic image, aerial image and SAR image were selected to evaluate the performance of the method, and the segmentation results were compared with presented hidden Markov tree segmentation method based on EM algorithm. For synthetic mosaic texture images, miss-classed probability was given as the evaluation to segmentation result. the experiment showed the method has better segmentation performance and more flexibility in real application compared with wavelet hidden Markov tree segmentation.

关键词： wavelet one class support vector machine Bayesian decision image segmentation multiresolution

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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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