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International Conference on Information Fusion

作者： Yan Liang Zengfu Wang Lili Wei Yongmei Cheng Quan Pan College of Automation Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9780662478300;0662478304

The estimation of Markov jump systems (MJS) is widely used in target tracking, fault detection, signal processing and digital communications. However, the above researches all assume that state measurement and additional mode observation are synchronous which means both state measurement and mode observation at each sampling time arrive at the fusion centre at the same time. The problem of estimation of MJS that mode observation is one-step lagged to its corresponding state measurement is considered. Along state-augmentation approach and the derivation of image-enhanced interacting multiple model (IE-IMM), a new generic estimation algorithm is proposed. It is shown by simulation result that the proposed algorithm is effective.

关键词： State estimation Target tracking Signal processing algorithms Time measurement Fault detection Digital signal processing Digital communication image sensors Smoothing methods Equations

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SAR image Segmentation Based on Mixture Context and Wavelet Hidden-Class-Label Markov Random Field

SAR Image Segmentation Based on Mixture Context and Wavelet ...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Ming Li Yan Wu Shunjun Wu National Key Lab of Radar Signal Processing Xidian University Xi'an Shaanxi China

Because of the property that synthetic aperture radar (SAR) image includes plenty of multiplicative speckle noise, a new segmentation algorithm is proposed based on the mixture context and the wavelet hidden-class-label Markov random field (MRF). In our paper, a wavelet mixture heavy-tailed model is constructed, then the hidden-class-label MRF is extended to the wavelet domain to suppress the effect of speckle noise. Since the multi-scale segmentation with overlapping window is presented here, this segmentation method is utilized at the finest scale of stationary wavelet transform (SWT) domain, and the classical segmentation method is also utilized at the coarse scales of discrete wavelet transform (DWT) domain. Finally, a mixture context model is proposed to estimate inter-scale parameters and the optimal segmentation result is derived from a new maximum a posteriori (MAP) classification. The experimental results show that our method achieves accurate SAR image segmentation result and preserves detailed boundary effectively.

关键词： image segmentation Markov random fields Discrete wavelet transforms Wavelet coefficients Speckle Synthetic aperture radar Signal processing algorithms Wavelet domain Hidden Markov models Gaussian distribution

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An automatic video object segmentation scheme

An automatic video object segmentation scheme

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IEEE International Symposium on Intelligent Signal processing and Communication systems (ISPACS)

作者： xiaoyan Zhang Lingxia Liu Xuchun Zhuang The Telecommunication Engineering Institute Air Force Engineering University Xi'an China

In this paper, a new scheme is presented for performing automatic video object segmentation. First of all, a new adaptive and reliable threshold estimation method has been proposed for the change detection process. Secondly, the initial edge model of the video object is obtained on the basis of the adaptive change detection and Canny edge detection. Thirdly, the edge model is tracked and updated in every frame to accommodate rotation and changes in shape of the tracked object and then the outer contour of the video object is determined based on the mathematical morphological operation. Lastly, an improved active contour which uses the gradient vector as the external force is applied to guide the initial outer contour moving to the actual video object contour. Experiment results show that the proposed algorithm is robust to the entire motion and local deformation of object, adaptable to the complex background and can get the closed and accurate video object contour.

关键词： Object segmentation image edge detection Robustness Change detection algorithms Reliability engineering Object detection Active contours Electric breakdown Video signal processing Mathematical model

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Laplacian Affinity Propagation for Semi-Supervised Object Classification

Laplacian Affinity Propagation for Semi-Supervised Object Cl...

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IEEE International Conference on image processing

作者： Yun Fu Zhu Li xi Zhou Thomas S. Huang Beckman Institute University of Illinois Urbana-Champaign Urbana-Champaign IL USA Motorola Laboratories Multimedia Research Laboratory Schaumburg IL USA Beckman Institute University of Illinois at Urbana-Champaign (UIUC) Urbana IL 61801 USA Dept. of ECE University of Illinois at Urbana-Champaign (UIUC) Urbana IL 61801 USA

We solve the semi-supervised multi-class object classification problem by a graph-based learning algorithm, called Laplacian affinity propagation (LAP). The idea is to model and train both labeled and unlabeled data by constructing a local neighborhood affinity graph in a smoothness formulation of Laplacian matrix, based on graph mincuts or harmonic energy minimization. The unknown labels for unlabeled data are inferred from an optimized graph embedding procedure subject to the labeled data. Such label-to-unlabel propagation scheme can provide a closed form solution via a learning framework that is flexible for any new design. LAP integrates embedding and classifier together and gives smooth labels with respect to the underlying manifold structure formed by the training data. Object classification experiments on COIL database demonstrate the effectiveness and applicability of such algorithm.

关键词： Laplace equations Closed-form solution Clustering algorithms Databases Training data Semisupervised learning Labeling Learning systems Information retrieval Pattern recognition

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Geometrical methods for lightness adjustment in YCC color spaces

Geometrical methods for lightness adjustment in YCC color sp...

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Conference on Color Imaging xi

作者： Samadani, Ramin Li, Guo HP Labs Imaging Syst Lab 1501 Page Mill RdMS 1203 Palo Alto CA 94304 USA

ISBN: (纸本)0819460982

Lightening or darkening an image is a fundamental adjustment used to improve aesthetics or correct exposure. This paper describes new geometrical algorithms for lightness adjustment, implementing fast traversal of colors along lightness-saturation curves, applicable when the data starts naturally in YCC space (JPEG images or MPEG videos). Here, YCC refers generically to color spaces with one luminance and two color difference channels, including linear YCC spaces and CIELAB. Our first solution uses a class of curves that allows closed-form computation. Further assuming that saturation is a separable function of luminance and curve parameter simplifies the computations. This approach reduces clipping and better adjusts lightness together with saturation. Preliminary evaluation with 96 images finds good subjective results, and clipping is reduced to about 510 of a prior approach.

关键词： RGB color model Color difference image analysis Video Imaging systems Light sources Printing Skin image compression Lithium

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Adaptive silhouette extraction in dynamic environments using fuzzy logic

Adaptive silhouette extraction in dynamic environments using...

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IEEE International Conference on Fuzzy systems

作者： Chen, xi He, Zhihai Keller, James M. Anderson, Derek Skubic, Marjorie Univ Missouri Dept Elect & Comp Engn Columbia MO 65203 USA

ISBN: (纸本)9780780394889

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction algorithms proposed in the Uterature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.

关键词： Fuzzy logic

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Object-based image recoloring using alpha matte and color histogram specification

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12th International Conference on Virtual systems and Multimedia, VSMM 2006

作者： Xuezhong, xiao Lizhuang, Ma Marco, Kunze Shanghai Jiao Tong University China DAI-Labor TU Berlin Germany

ISBN: (纸本)3540463046

With the increasing popularity of digital cameras, image editing tools for amateur users are needed. Patrick Pérez and his colleagues generalized that image editing tasks concern either global changes or local changes confined to a selection[1]. Here we focus on local editing of images' color-object-based image recoloring. In this paper, we introduce a framework for recoloring destination region (desired object) with desired color, which integrates alpha matting and compositing and color transformation algorithms to provide an object-based recoloring tool for common users. There are three steps in the framework: matte extraction, color transformation and alpha compositing. We present a fast color histogram specification as a general method for example-based color transformation. In some cases, our framework to the recoloring problem are quite simple but works well. We thus feel that the primary contribution of this paper may lie in the organic integration of algorithms totally. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Color image processing

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Pervasive surveillance using a cooperative mobile sensor network

Pervasive surveillance using a cooperative mobile sensor net...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Huntwork, Michael Goradia, Amit xi, Ning Haffner, Clayton Klochko, Chad Mutka, Matt Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)0780395050

A distributed processing and communication approach is applied to networked surveillance systems providing an extended perception and sensing capability in monitored environments. By pervasive we mean that the entire surveyed area is covered by a heterogeneous collection of fixed and mobile sensors. The sensor network uses a cooperative tracking technique that allows for the deployment of mobile sensors based on data provided by other sensors in the network. Also, an image-based tracking technique is used for tracking when the target is in view. The task of tracking multiple targets in a distributed surveillance network is a challenging problem because of the following reasons: (1) multiple targets need to be monitored and tracked continuously and must remain in view of at least one of the sensors;(2) the view of the sensors needs to be optimized so that the targets are observed with a discernible resolution for feature identification;(3) it is important to devise stable control algorithms for accomplishing the surveillance task;(4) assigning tracking tasks to sensors must consider load balancing and efficient use of all sensors. This paper presents a distributed communication and processing model that allows for a fast deployment of sensor nodes and implementation of ad hoc tracking in a multi-target surveillance scenario. Also, experimental results demonstrate the efficacy of the proposed approach for tracking multiple targets over a large area with fixed and mobile sensors.

关键词： surveillance networks visual surveillance distributed tracking Hausdorff tracking target tracking

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Optimal Filtering based Shift Estimation for Fringe Pattern Profilometry by Generalized Analysis Model

Optimal Filtering based Shift Estimation for Fringe Pattern ...

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IEEE International Symposium on Intelligent Signal processing and Communication systems (ISPACS)

作者： Yingsong Hu Jiangtao xi Joe Chicharo Zongkai Yang Department of Electronic and Information Engineering Huazhong University of Science and Technology Wuhan China School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW Australia

This paper presents a new algorithm for fringe pattern profilometry by utilizing generalized analysis model, called optimal filtering based shift estimation (OFSE) method, which provides much lower complexity compared with traditional methods. Meanwhile, as OFSE is derived based on the generalized analysis model, the reconstruction results will not be influenced by the nonlinearity of fringe pattern projection and acquisition system. The efficiency of the proposed OFSE method is confirmed by simulation results, which show that the accuracy of three-dimensional reconstruction using digital fringe pattern profilometry technique can be much improved and the computational complexity can be significantly reduced

关键词： Pattern analysis Algorithm design and analysis Surface reconstruction Nonlinear distortion image reconstruction Signal processing algorithms Computational complexity Information filtering Information filters Information analysis

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一种改进的自适应局部噪声消除滤波算法

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系统工程理论与实践 2006年第12期26卷 99-104页

作者：赵小明叶喜剑姚敏台州学院计算机系浙江大学计算机学院杭州310027

指出了美国冈萨雷斯教授(Rafael ***)提出的一个基于自适应局部噪声消除滤波器算法存在的不足,并提出了一种改进的自适应局部噪声消除滤波新算法.通过比较可以发现改进的算法要比原算法的均方误差emse降低了一个数量级,而且各种信噪比SN... 详细信息

指出了美国冈萨雷斯教授(Rafael ***)提出的一个基于自适应局部噪声消除滤波器算法存在的不足,并提出了一种改进的自适应局部噪声消除滤波新算法.通过比较可以发现改进的算法要比原算法的均方误差emse降低了一个数量级,而且各种信噪比SNR、SNRm、PSNR都将比原算法提高了1/4.计算机仿真实验证明了改进的自适应局部噪声消除滤波算法是有效的.

关键词：自适应局部噪声滤波器

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