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检索条件"任意字段=MIPPR 2007: Automatic Target Recognition and Image Analysis;and Multispectral Image Acquisition"

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Adaptive four windows wavelet image denoising based on local polynomial approximation-intersection of confidence intervals - art. no. 67862H

Adaptive four windows wavelet image denoising based on local...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Feng, Hongxiao Hou, Biao Jiao, Licheng Li, Haigang Xidian Univ Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819469502

Local Polynomial Approximation-Intersection of Confidence Intervals (LPA-ICI) is a new approach, which can find the boundary of the isotropic region efficiently, especially for noisy images. This paper presents a novel image denoising method, adaptive four windows wavelet image denoising based on LPA-ICI, which is composed of three parts: searching for four adaptive windows with LPA-ICI, updating the noisy wavelet coefficients by hard threshold and obtaining a final "clean" pixel value by fusing the updated pixels with different weights which are determined by the sparsity of regions. Experiments show that our algorithm has advanced performance, reconstructed edges are clean, and especially without unpleasant ringing artifacts.

关键词： image denoising adaptive window Local Polynomial Approximation-Intersection of Confidence Intervals weighting according to sparsity

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Interactive dynamic graph cut based image segmentation with shape priors

Interactive dynamic graph cut based image segmentation with ...

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Liu, Chen Li, Fengxia Zhan, Shouyi Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9780819469502

In this paper, we present a new method bases on dynamic graph cut and captures both the shape and the nature information of the image for interactive image segmentation. While traditional interactive graph cut approaches for image segmentation are often successful, they may fail in camouflage. Prior shape knowledge can largely mitigate this problem. In this paper, two kinds of shape priors are taken into account to obtain more accurate results. In order to use the information from user input more effectively, a weight function is introduced to control the relative importance of shape knowledge. Then, a one-shot fully dynamic graph cut algorithm is introduced to minimize the energy function, and during this procedure, only a subset of pixels in the image is considered, which greatly reduces the complexity of dynamic graph cut algorithm. Extensive experiments, including comparisons with some state-of-the-arts, show the effectiveness of our methods in improving the segmentation performance and saving the processing time.

关键词： image segmentation shape prior dynamic graph cut

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Method to reduce over-segmentation of images using immune clonal algorithm - art. no. 678649

Method to reduce over-segmentation of images using immune cl...

引用

5th International Symposium on multispectral image Processing and Pattern recognition

作者： Liang, Jianhua Wang, Shuang Jiao, Licheng Xidian Univ Inst Intelligent Informat Proc Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9780819469502

image segmentation is a difficult task. These years, researchers have proposed many segmentation methods based on Evolutionary Algorithms, but most of them used Evolutionary Algorithms to optimize the parameters of an existing segmentation algorithm. This paper tries to use the Evolutionary Algorithms to segment images expecting to explore a new way of image segmentation. The method described in the paper pre-segments the image by Watersheds and then merges it by Immune Clonal Algorithm (ICA). To implement the task, several operators are proposed such as the DC operator, the Proportional Creation of the First generation operator, and fitness function based on JND and average gray value. In the end, the proposed method is compared with another method using GA. The experiments show that the method is effective and the work is significant.

关键词： image segmentation over-segmentation evolutionary algorithm immune clonal algorithm

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ISAR imaging at low SNR level based on polarimetric whitening filter

ISAR imaging at low SNR level based on polarimetric whitenin...

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8th Symposium on multispectral image Processing and Pattern recognition (mippr) - multispectral image acquisition, Processing, and analysis

作者： Liu, Yang Li, Gang Tian, Biao Chen, Zeng-ping Natl Univ Def Technol ATR Key Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9780819498021

ISAR (inverse synthetic aperture radar) can generate 2D image of a non-cooperative moving target and be used for military and civilian purpose. For moving target, well-focused ISAR images can be achieved using appropriate motion compensation and image reconstruction algorithm. The ISAR image will be difficultly when the echo is at a low signal-to-noise level. For polarimetric ISAR system which transmits and receives different polarisation signal in both two channels, the fusion of HRRP (high range resolution profile) from different channels will improve significantly the SNR of the signal. The method proposed in this paper firstly fuses the different channels HRRP to get a higher SNR signal. Then the target region is extracted in fused HRRP. Finally, the motion compensation for ISAR imaging is carried out only on the extracted target region data in the sense of using image reconstruction method to gain a focused ISAR image. The real measured data shows the validity of the algorithm.

关键词： ISAR imaging polarimetric whitening filter HRRP fusion target region extracting motion compensation

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Real time object tracking using adaptive kalman particle filter

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

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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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Moving point target detection using temporal variance filter in IR imagery

Moving point target detection using temporal variance filter...

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Sun, Xiechang Zhang, Tianxu Li, Meng Huazhong Univ Sci & Technol State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

A crucial problem in Infrared Search and Track (IRST) systems is the detection of moving point targets, there are many algorithms reported in the literature for dealing with this problem, yet none of them yield acceptable results under all situations. In this paper, we describe a new temporal variance filter (TVF) for detecting targets whose velocity are higher than 1 pixel/frame;the filter iteratively estimates the temporal variance of each pixel, then subtracts the last iteration step variance from the variance of current step. Subsequently, we introduce a novel image segmentation algorithm in order to extract point targets from clutter background, the trajectories of the point targets could be established by post-processing algorithm. Before applying the temporal filter, the anti max-median filter given by Suyog D. Deshpande et al. is incorporated as a preprocessing technique to suppress cloud clutter. It is assumed that targets' velocity is higher than 1 pixel/frame;targets with sub-pixel/frame velocity are not considered. The performance of our approach is evaluated by using available real-world infrared image sequences containing simulated moving point targets;it performs steadily under most situations.

关键词： point target detection temporal variance filter noise rejection image segmentation visual contrast IR imagery

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Adaptive tracking method for ground target of FLIR imaging

Adaptive tracking method for ground target of FLIR imaging

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mippr 2009 - automatic target recognition and image analysis: 6th International Symposium on multispectral image Processing and Pattern recognition

作者： Li, Yun Zhang, Tianxu Zuo, Zhengrong Wan, Meijun State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478061

An adaptive tracking method for ground target in FLIR image including centric and eccentric tracking is presented in this paper. The eccentric tracking is adopted to assist ATR or manual target acquisition by stabilizing the light axis of the seeker. The tracking adaptability detection at the start of centric tracking avoids tracking bad locked point and decreases the shift, slide and jump in the subsequent tracking. Combination of periodic template-updating based on scale variance rate and interruptive template-updating based on supervision of tracking point enhances the adaptability of tracking template. Supervision, modification of current tracking point and the combination of direct and indirect tracking increase the stability of tracking system. The multi-scale and size-variable template is adopted to fit the variable target scale. The detection and adoption of occlusion mask increase the accuracy of the tracking system. The hardware system based on multi-DSP takes a real-time parallel processing. The trial results show that this method is efficient to reduce the possibility of shift, slide, and jump in ground target tracking comparing with the traditional correlation tracking method. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： target tracking

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Approach of image segmentation with edge gradient estimate

Approach of image segmentation with edge gradient estimate

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Li, Yue-e Xu, Yang Chen, Yingming Shanxi Univ Dept Elect & Informat Sci Taiyuan 030006 Shanxi Peoples R China Shanxi Univ Dept Elect & Informat Sci Taiyuan 030006 Peoples R China Grad Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9780819469502

Gradient amplitude and phase can't be used together in traditional edge detection procedure, So an algorithm for image segmentation based on edge gradient estimate is given in this paper. First, a standard template is ascertained by the gradient phase of the center pixel;then, estimate edge gradient is the absolute value of the correlation coefficient between the normalized data vector and the template. The method of non-maximum suppression is used when determining pixel local maximum point;hysteresis threshold is used when determining edge point. In both the treatment process, the normalized gradient amplitude and phase would regard as distinguish basis, so this will increase the capability of weak edge detection, and it can suppress the noise impact. This paper gives the relevant experiment result.

关键词： edge detection gradient estimation template image segmentation hysteresis threshold non-maximum suppression gradient amplitude phase pixel noise

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High resolution satellite imagery segmentation based on adaptively integrated multiple features

High resolution satellite imagery segmentation based on adap...

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Wang, Aiping Tian, Peng Wang, Shugen Wuhan Univ Sch Remote Sensing Informat Engn 129 Luoyu Rd Wuhan 430079 Peoples R China Beijing Informat Resource Management Ctr Beijing 100082 Peoples R China

ISBN: (纸本)9780819469502

automatic segmentation of high resolution satellite (HRS) imagery is the first step and a very important part of object-oriented approaches. The HRS sensors increase the spectral within-field heterogeneity and the structural or spatial details of images. Spatial features are important to HRS image analysis in addition to spectral information. This paper presents a novel feature extraction method and evaluates its performance on segmentation of HRS images based on adaptively integrating multiple features. The first two principal component (PC) images are obtained by principal component analysis (PCA) of a multispectral image and used to calculate the texture and spectral distributions of a region, which are denoted by two-dimensional (2D) histograms. The 2D texture histogram of a region is the joint distribution of its two texture labeled images calculated by rotation invariant local binary pattern (LBP) operator. The spectral distribution of a region is the joint distribution of the pixel values of its two PC images after normalization. The color feature is a 2D hue/saturation histogram that is computed through IHLS color space. The three features are integrated by a weighted sum similarity measure and used to hierarchical splitting, modified agglomerative merging and boundary refinement segmentation framework. The segmentation scheme based on adaptively integrating multiple features demonstrates promising results.

关键词： high resolution satellite (HRS) imagery image segmentation principal component analysis (PCA) feature distributions

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Joint deconvolution of adaptive optics images from slope measurements of wavefront sensor

Joint deconvolution of adaptive optics images from slope mea...

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5th International Symposium on multispectral image Processing and Pattern recognition

作者： Chen Bo Geng Ze-xun Yang Yang Zhang Ya-xin Zhao Zhen-lei Informat Engn Univ Inst Surveying & Mapping 66 Longhai Rd Zhengzhou 450052 Peoples R China Guilin AF Acad Guilin 541003 Peoples R China

ISBN: (纸本)9780819469502

The atmospheric turbulence severely limits the angular resolution of ground based telescopes. When using Adaptive Optics (AO) compensation, the wavefront sensor data permit the estimation of the residual PSF. Yet, this estimation is imperfect, and a deconvolution is required for reaching the diffraction limit. It is a powerful and low-cost high-resolution imaging technique designed to compensate for the image degradation due to atmospheric turbulence. A joint deconvolution method based on slope measurements for AO image is presented. It deduces from a Bayesian framework in the context of imaging through turbulence with adaptive optics. It takes into account the noise in the images and in the Hartmann-Shack wavefront sensor measurements and the available a priori information on the object to be restored as well as on the wave fronts. Deconvolution results are presented for experimental data.

关键词： adaptive optics image deconvolution slope measurements Hartmann-Shack wavefront sensor

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