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Study on Statistics Iterative Thresholding Segmentation Based on Aviation image

Study on Statistics Iterative Thresholding Segmentation Base...

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Guang Yang Kexiong Chen Maiyu Zhou Zhonglin Xu Yongtian Chen The Special Profession Department Aviation University슠of Air슠Force Changchun China

image segment is one of the most fundamental and difficult problems in image analysis and computer vision. The image thresholding problem is treated as an important issue in image processing, and it can not only reduce the image data, but also lay a good foundation for succedent target recognition and image understanding. Character of global thresholding segmentation and local thresholding was analyzed in image segmentation. A new thresholding statistic iterative arithmetic is presented to overcome the direct worth method in thresholding, aiming at some lighting asymmetry and the abrupt a blemish for, or bigger arithmetic figure in ratio in a variety in gray of background image. Statistics iterative thresholding segmentation, based on image gray histogram and Gauss statistics distributing, obtain the theory expression of statistics iterative method and the best worth thresholding method and steps. Aviation image was thresholding segmentation using statistic iterative arithmetic, histogram technique and adaptive method respectively. Compared three thresholding results, it shows that statistic iterative method greatly improved the anti-noise capability of image segmentation and had the good result to the image of the worth and not easy to segment in full value thresholding method.

关键词： Statistics image segmentation Statistical distributions Iterative methods Arithmetic image analysis Histograms Computer vision image processing Target recognition

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image Reconstruction using Microwave Tomography for Breast Cancer Detection on distributed Memory Machine

Image Reconstruction using Microwave Tomography for Breast C...

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International Conference on parallel processing (ICPP)

作者： Meilian Xu Abas Sabouni Parimala Thulasiraman Sima Noghanian Stephen Pistorius Department of Computer Science University of Manitoba Winnipeg MAN Canada Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada Cancer Care Manitoba Winnipeg MAN Canada

Breast cancer, with the exception of lung cancer, is the leading cause of cancer deaths in women. It is also one of the few cancers that can be controlled by using asymptomatic screening method, followed by effective treatments. One recent screening modality under development, microwave tomography, uses the apparent dielectric property contrasts between different breast tissues at microwave frequencies. Microwave tomography uses a numerical model and the image reconstruction consists of iteratively searching the breast structures, applying the numerical model to the breast structures, and matching measured data with computation results of the model. This paper focuses on finite-difference time-domain (FDTD) for the numerical model and genetic algorithm (GA) for the iterative searches. FDTD and GA are time-consuming, yet they are data parallel in nature. In this paper, a parallel algorithm integrating GA and FDTD for detecting tumors using microwave tomography technique is presented. The algorithm is implemented on distributed memory machines using MPI.

关键词： image reconstruction Tomography Breast cancer Cancer detection Finite difference methods Time domain analysis Numerical models Lungs Dielectrics Breast tissue

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Region-based image Fusion by Using Region Priorities

Region-based Image Fusion by Using Region Priorities

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Zhang Yingjie Ge Liling School of Mechanical Engineering Xi''an Jiaotong University Xi'an Shaanxi China School of Mechanical Engineering Xi''an University of Technology Xi'an Shaanxi China

In this paper, a new region-based approach to multi-resolution image fusion is presented. To make fusion decision better, a master image is selected from the source images. Then, it is segmented using the background subtraction based Mumford and Shah algorithm, which significantly improves the segmentation at quality and convergence speed. After that, the fusion rules on every region of the image are determined based on the best priority-first strategy. Moreover, the fusion on the whole image domain is replaced by using different rules on every segmented region of the image. The corresponding regions in other images are located by linear mapping methods. Some numerical results have been given and compared to the pixel-based fusion methods. Despite an increase in complexity, more intelligent fusion rules may be applied to remove side effects like reducing contrast and sensitive to error of registration.

关键词： image fusion image segmentation Discrete wavelet transforms image sensors Sensor fusion Space technology Nonlinear distortion Principal component analysis Signal processing Infrared image sensors

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Survey of parallel MATLAB Techniques and Applications to Signal and image processing

Survey of Parallel MATLAB Techniques and Applications to Sig...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ashok Krishnamurthy John Nehrbass Juan Carlos Chaves Siddharth Samsi Ohio Supercomputer Center Columbus OH USA

We present a survey of modern parallel MATLAB techniques. We concentrate on the most promising and well supported techniques with an emphasis in SIP applications. Some of these methods require writing explicit code to perform inter-processor communication while others hide the complexities of communication and computation by using higher level programming interfaces. We cover each approach with special emphasis given to performance and productivity issues.

关键词： MATLAB Signal processing image processing Computer languages Productivity Licenses High performance computing distributed computing Signal processing algorithms Concurrent computing

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A Genetic Approach for Distributing Semantic Databases of Crowd Simulations

A Genetic Approach for Distributing Semantic Databases of Cr...

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International Symposium on parallel and distributed processing (IPDPS)

作者： M. Lozano J. M. Orduna V. Cavero Departamento de Informática Universidad de Valencia Valencia Spain

Last years have witnessed how crowd simulations have become an essential tool for many virtual environment applications. These applications require both rendering visually plausible images and managing the behavior of autonomous agents, and therefore they need a scalable design that allows them to simultaneously tackle these two requirements. One of the main problems in the design of a scalable crowd simulation consists of efficiently distributing among different computers the semantic database containing the virtual world. In this paper, we propose a genetic approach for distributing the semantic database of crowd simulations in such a way that the dependencies among the computers hosting the pieces of the database are minimized. The proposed approach avoids the saturation of these computers by ensuring that the size of the pieces assigned to each computer is properly balanced. The performance evaluation results show that the proposed approach significantly reduces the resulting overhead in regard to other local search methods, regardless of the movement pattern of the agents. Therefore, it allows an effective partition of the semantic database.

关键词： Genetics image databases Visual databases Computational modeling Computer simulation distributed computing Virtual environment Application software Rendering (computer graphics) Autonomous agents

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A parallel CBIR Implementation using Perceptual Grouping of Block-Based Visual Patterns

A Parallel CBIR Implementation using Perceptual Grouping of ...

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IEEE International Conference on image processing

作者： Shyi-Chyi Cheng Wei-Kan Huang Yu-Jhih Liao Da-Chun Wu Department of Computer Science and Engineering National Taiwan Ocean University Taiwan Department of Computer and Communication Engineering National Kaohsiung First University of Science and Technology Taiwan

This paper proposes a parallel solution to retrieve images from distributed data sources using perceptual grouping of block-based visual patterns. The method of grouping visual patterns into image model based on generalized Hough transform is one of the most powerful techniques for image analysis. However, real-time applications of this method have been prohibited due to the computational intensity in similarity searching from a large centralized image collection. A query object is decomposed into non-overlapped blocks, where each of them is represented as a visual pattern obtained by detecting the line edge from the block using the moment-preserving edge detector. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data. In this work, we describe a heterogeneous cluster-oriented CBIR implementation. First, the workload to perform an object search is analyzed, and then, a new load balancing algorithm for the CBIR system is presented. Simulation results show that the proposed method gives good performance and spans a new way to design a cost-effective CBIR system.

关键词： image edge detection Object detection Information retrieval image retrieval Detectors Voting Search methods Performance analysis Algorithm design and analysis Load management

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OpenVL: Towards A Novel Software Architecture for Computer Vision

OpenVL: Towards A Novel Software Architecture for Computer V...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changsong Shen Sidney S. Fels James J. Little Department of Electrical and Computer Engineering University of British Columbia Canada Department of Computer Science University of British Columbia Canada

This paper presents our progress on OpenVL -a novel software architecture to address efficiency through facilitating hardware acceleration, reusability and scalability for computer vision. A logical image understanding pipeline is introduced to allow parallel processing. As well, we discuss our middleware -VLUT that enables applications to operate transparently over a heterogeneous collection of hardware implementations. OpenVL works as a state machine, with an event-driven mechanism to provide users with application-level interaction. Various explicit or implicit synchronization and communication methods are supported among distributed processes in the logical pipelines. The intent of OpenVL is to allow users to quickly and easily recover useful information from multiple scenes across various software environments and hardware platforms. We implement two different human tracking systems to validate the critical underlying concepts of OpenVL.

关键词： Software architecture Computer vision Hardware Pipelines Acceleration Scalability parallel processing Middleware Application software Layout

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基于网格采样的深度图像表面特征提取算法

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中国图象图形学报 2007年第6期12卷 1091-1097页

作者：孙晓兰赵慧洁北京航空航天大学仪器科学与光电工程学院北京100083

深度图像的高级特征提取是3维视觉一个重要研究领域。为了提高对大量离散3维数据的处理速度,简化算子结构,本文提出一种新的方法,将单目或多目结构光传感器采集的离散3维数据点云转换成深度图像形式,使z方向代表深度,并实现x、y方向数... 详细信息

深度图像的高级特征提取是3维视觉一个重要研究领域。为了提高对大量离散3维数据的处理速度,简化算子结构,本文提出一种新的方法,将单目或多目结构光传感器采集的离散3维数据点云转换成深度图像形式,使z方向代表深度,并实现x、y方向数据的规则网格采样。接着提出一种深度图像分割算法,先对整幅图像进行边缘提取,得到阶跃和褶皱两种边缘,采用主成分分析法(PCA)通过计算欧氏距离得到面的法线方向和方向间的夹角。经过两类传感器采集到的大量图像实验证明,该算法明显优于单纯的边缘或者区域算法,具有良好的抗噪声性能,满足并行在线测量的要求,并且不受物体形状的约束,可以应用于3D模型重建、机器人自主导航、逆向工程、文物数字化等多个3维视觉领域。

关键词：网格化深度图像分割特征提取主成分分析

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A parallel mutual information based image registration algorithm for applications in remote sensing 4th

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4th International Symposium on parallel and distributed processing and Applications

作者： Du, Yunfei Zhou, Haifang Wang, Panfeng Yang, Xuejun Liu, Hengzhu Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783540680673

image registration is a classical problem that addresses the problem of finding a geometric transformation that best aligns two images. Since the amount of multisensor remote sensing imagery are growing tremendously, the search for matching transformation with mutual information is very time-consuming and tedious, and fast and automatic registration of images from different sensors has become critical in the remote sensing framework. So the implementation of automatic mutual information based image registration methods on high performance machines needs to be investigated. First, this paper presents a parallel implementation of a mutual information based image registration algorithm. It takes advantage of cluster machines by partitioning of data depending on the algorithm's peculiarity. Then, the evaluation of the parallel registration method has been presented in theory and in experiments and shows that the parallel algorithm has good parallel performance and scalability.

关键词： image registration

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A parallel unmixing algorithm for hyperspectral images

A parallel unmixing algorithm for hyperspectral images

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Conference on Intelligent Robots and Computer Vision XXIV - Algorithms, Techniques, and Active Vision

作者： Robila, Stefan A. Maciak, Lukasz G. Montclair State Univ Dept Comp Sci Ctr Imaging & Opt RI 301 Montclair NJ 07043 USA

ISBN: (纸本)0819464821

We present a new algorithm for feature extraction in hyperspectral images based on source separation and parallel computing. In source separation, given a linear mixture of sources, the goal is to recover the components by producing an unmixing matrix. In hyperspectral imagery, the mixing transform and the separated components can be associated with endmembers and their abundances. Source separation based methods have been employed for target detection and classification of hyperspectral images. However, these methods usually involve restrictive conditions on the nature of the results such as orthogonality (in Principal Component Analysis - PCA and Orthogonal Subspace Projection - OSP) of the endmembers or statistical independence (in Independent Component Analysis - ICA) of the abundances nor do they fully satisfy all the conditions included in the Linear Mixing Model. Compared to this, our approach is based on the Nonnegative Matrix Factorization (NMF), a less constraining uninixing method. NMF has the advantage of producing positively defined data, and, with several modifications that we introduce also ensures addition to one. The endmember vectors and the abundances are obtained through a gradient based optimization approach. The algorithm is further modified to run in a parallel environment. The parallel NMF (P-NMF) significantly reduces the time complexity and is shown to also easily port to a distributed environment. Experiments with in-house and Hydice data suggest that NMF outperforms ICA, PCA and OSP for unsupervised endmember extraction. Coupled with its parallel implementation, the new method provides an efficient way for unsupervised unmixing further supporting our efforts in the development of a real time hyperspectral sensing environment with applications to industry and life sciences.

关键词： hyperspectral images blind source separation Nonnegative Matrix Factorization Linear Mixing Model parallel processing image processing

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