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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on parallel and distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)

作者： Bi, Xiuli Pun, Chi-Man Yuan, Xiao-Chen Univ Macau Dept Comp & Informat Sci Taipa Macau Sar Peoples R China

ISBN: (纸本)9781509032051

In this paper, an Adaptive Polar based Filtering Method is proposed for image copy-move forgery detection. In order to improve the performance of detection method, the post-processing of the matching results is worth being focused on. To filter out the redundant pixels from the initially matched pixels, two pixels sets-Symmetrical Matched Pixels set and Unsymmetrical Matched Pixels set, are extracted from the matched pixel pairs;furthermore, the polar distributions of the two sets are calculated respectively. Then, the filtering thresholds can be adaptively calculated according to the polar distribution, thus the redundant pixels can be filtered out accordingly. Finally, some morphological operations are applied to the remained pixels to generate the detected forged regions. Experimental results show that the proposed scheme can achieve much better detection results compared with the existing state-of-the-art copy-move forgery detection methods.

关键词： Polar Distribution Adaptive Polar based Filtering Method Copy-Move Forgery

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Bilateral histogram equalization with pre-processing for contrast enhancement

Bilateral histogram equalization with pre-processing for con...

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

作者： Feroz Mahmud Amil Md. Mostafijur Rahman Shanto Rahman Emon Kumar Dey Mohammad Shoyaib Institute of Information Technology University of Dhaka Institute of Information Technology University of Dhaka Bangladesh

Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this problem, we have introduced a new image enhancement technique namely Bilateral Histogram Equalization with Pre-processing (BHEP) which uses Harmonic mean to divide the histogram of the image. We have performed both qualitative and quantitative measurements for experiments and the results show that BHEP creates less artifacts in several standard images than the existing state-of-the-art image enhancement techniques.

关键词： Histograms Mathematical model image enhancement Harmonic analysis image color analysis Adaptive equalizers image edge detection

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复频域实时重建医学光声图像的研究

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南京大学学报（自然科学版） 2016年第3期52卷 536-541页

作者：余双春袁杰南京大学电子科学与工程学院南京210093

提出将光声成像中的傅里叶域重建方法与分布式并行计算相结合的方法,应用于实际处理医学成像中,实现实时重建医学光声图像的目的.滤波反投影算法是光声图像重建的一个广泛使用的算法,但由于在时域重建的时间复杂度大,并不适合实时重建.... 详细信息

提出将光声成像中的傅里叶域重建方法与分布式并行计算相结合的方法,应用于实际处理医学成像中,实现实时重建医学光声图像的目的.滤波反投影算法是光声图像重建的一个广泛使用的算法,但由于在时域重建的时间复杂度大,并不适合实时重建.傅里叶域重建方法相对于滤波反投影算法具有时间复杂度上的优越性,且产生较少的伪影.并行处理技术,已经广泛应用于各类工程学科,其影响力及范围随着研究深入逐步扩大.分布式并行计算将提高图像处理速度,使实时重建医学图像成为可能.将MATLAB作为平台,实现了对医学图像在复频域的并行加速处理,给出了并行处理与串行处理的测试结果的比较.实验结果表明:傅里叶域重建方法与分布式并行计算的结合,使医学光声成像重建时间大大缩短.

关键词：光声成像傅里叶域重建并行处理技术分布式计算

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Medical image segmentation on GPUs - A comprehensive review

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MEDICAL image ANALYSIS 2015年第1期20卷 1-18页

作者： Smistad, Erik Falch, Thomas L. Bozorgi, Mohammadmehdi Elster, Anne C. Lindseth, Frank Norwegian Univ Sci & Technol N-7491 Trondheim Norway SINTEF Med Technol N-7465 Trondheim Norway

Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from CPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： Medical image Segmentation GPU parallel

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parallel Graph Coloring for Manycore Architectures

Parallel Graph Coloring for Manycore Architectures

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International Symposium on parallel and distributed processing (IPDPS)

作者： Mehmet Deveci Erik G Boman Karen D. Devine Sivasankaran Rajamanickam Sandia National Laboratories Albuquerque NM

ISBN: (纸本)9781509021413

Graph algorithms are challenging to parallelize on manycore architectures due to complex data dependencies and irregular memory access. We consider the well studied problem of coloring the vertices of a graph. In many applications it is important to compute a coloring with few colors in near-lineartime. In parallel, the optimistic (speculative) coloring method by Gebremedhin and Manne is the preferred approach but it needs to be modified for manycore architectures. We discuss a range of implementation issues for this vertex-based optimistic approach. We also propose a novel edge-based optimistic approach that has more parallelism and is better suited to GPUs. We study the performance empirically on two architectures(Xeon Phi and GPU) and across many data sets (from finite element problems to social networks). Our implementation uses the Kokkos library, so it is portable across platforms. We show that on GPUs, we significantly reduce the number of colors (geometric mean 4X, but up to 48X) as compared to the widely used cuSPARSE library. In addition, our edge-based algorithm is 1.5 times faster on average than cuSPARSE, where it hasspeedups up to 139X on a circuit problem. We also show the effect of the coloring on a conjugate gradient solver using multi-colored Symmetric Gauss-Seidel method as preconditioner, the higher coloring quality found by the proposed methods reduces the overall solve time up to 33% compared to cuSPARSE.

关键词： Color Instruction sets Computer architecture Graphics processing units Algorithm design and analysis image color analysis Libraries

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Connecting functional brain imaging and parallel distributed processing

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LANGUAGE COGNITION AND NEUROSCIENCE 2015年第4期30卷 380-394页

作者： Cox, Christopher R. Seidenberg, Mark S. Rogers, Timothy T. Univ Wisconsin Dept Psychol Madison WI 53706 USA

Functional neuroimaging and parallel distributed processing (PDP) theory, both introduced to cognitive science in the 1980s, led to influential research programmes that have proceeded in parallel with little mutual influence. The PDP approach advanced specific claims about the nature of neural representations that, perhaps surprisingly, have gone largely untested in functional brain imaging. One reason may be the widespread use of univariate statistical methods for analysing brain imaging data, which typically rely on assumptions that render them unable to detect distributed representations of the kind that PDP predicts. More recent multivariate methods for image analysis may be better suited to detecting such representations. In the current article, we consider why univariate methods have been insufficient to test PDP's representational claims, articulate some of the properties that neural representations ought to have if the PDP view is valid and then survey the recent neuroimaging literature for evidence that neural representations do or do not have these properties. The survey establishes that the PDP view of distributed representations has considerable evidential support. This analysis underscores the importance of understanding how the assumptions underlying methods for analysing functional imaging data constrain the kinds of questions that can be addressed. We then consider the implications for our developing understanding of the neural bases of cognition and for the design of future brain imaging studies.

关键词： fMRI distributed representations MVPA PDP

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Maturity and disease detection in tomato using computer vision

Maturity and disease detection in tomato using computer visi...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Tanvi Mehra Vinay Kumar Pragya Gupta Dept. of Electronics and Communication Engineering Thapar University Patiala India Dept. of Electronics and Communication Engineering Jaypee University of Information Technology Waknaghat Himachal Pradesh India

ISBN: (纸本)9781509036707

Our country has vast potential to come up as a cardinal exporter of agricultural produce, but lack of quick quality evaluation techniques, huge losses in processing and handling after harvesting, diseased crop etc. result in a lower contribution to global market. The tomato crop is often infected by a disease, where plant's leaves get covered with spots of colors dark brown with purple border and light grey center; termed as Septoria Leaf Spot. It causes the leaves to turn yellow, but most damage occurs due to loss of leaves by infection. In this paper, tomato maturity based on color and fungal infection in the tomato leaves is determined. Initially thresholding algorithm was performed to determine the maturity of tomato. To make the system more generalized and self-adapting a shift to k-means clustering algorithm is made. Finally a comparative analysis of both the methods was done to analyze which method is more suitable in different conditions. Also an unconventional machine vision system has been suggested that scrutinizes the leaves emerging out of the soil and depending upon leaf spots, it analyzes the nature of fungus and its depth into the stem of tomato. k-means algorithm along with thresholding is used for segmentation of image and eventually identifying fungus. The fungus part that is segmented, is then studied to derive the percentage of presence.

关键词： Decision support systems Clustering algorithms Diseases Agriculture image color analysis Algorithm design and analysis image segmentation

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A Review on parallel Medical image processing on GPU 4

A Review on Parallel Medical Image Processing on GPU

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4th International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Khor, Hui Liang Liew, Siau-Chuin Zain, Jasni Mohd. Univ Malaysia Pahang Fac Comp Syst & Software Engn Lebuhraya Tun Razak Kuantan 26300 Pahang Darul Ma Malaysia

ISBN: (纸本)9781467367226

An efficient implementation are necessary, as most medical imaging methods are computational expensive, and the amount of medical imaging data is growing. Graphic processing units (GPUs) can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. This review investigates the use of GPUs to accelerate medical imaging methods. A set of criteria for efficient use of GPUs are defined. The review concludes that most medical image processing methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.

关键词： parallel computing Medical image processing GPU multicores

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Local feature point extraction for quantum images

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QUANTUM INFORMATION processing 2015年第5期14卷 1573-1588页

作者： Zhang, Yi Lu, Kai Xu, Kai Gao, Yinghui Wilson, Richard Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

Quantum image processing has been a hot issue in the last decade. However, the lack of the quantum feature extraction method leads to the limitation of quantum image understanding. In this paper, a quantum feature extraction framework is proposed based on the novel enhanced quantum representation of digital images. Based on the design of quantum image addition and subtraction operations and some quantum image transformations, the feature points could be extracted by comparing and thresholding the gradients of the pixels. Different methods of computing the pixel gradient and different thresholds can be realized under this quantum framework. The feature points extracted from quantum image can be used to construct quantum graph. Our work bridges the gap between quantum image processing and graph analysis based on quantum mechanics.

关键词： Quantum image Feature extraction Quantum adder Quantum image transformation

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A novel three-dimension fast radar imaging method based on far-field-approximation

A novel three-dimension fast radar imaging method based on f...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Long Gang Wang Lianlin Li Tie Jun Cui Peking University Beijing Beijing CN Southeast University Nanjing Jiangsu CN

Three-dimension imaging and identification with radar has recently gained a lot of attention. Resulting from the over-simple imaging model and over-large computational complexity, the existing three-dimension radar imaging methods are limited in few scenarios. To break above bottlenecks, we introduce a novel physics-driven three-dimension fast radar imaging method based on far-field-approximation assumption, in which the whole imaging region would be decomposed with a series of overlapping-patches to accelerate the imaging speed in parallel. The proposed method has four key steps: e.g., first, the whole imaging region would be divided into a series of overlapping-patches, which indicates that the large-scale imaging problem is efficiently decomposed into a set of small-scale imaging sub-problems and can be solved in the parallel or distributed manner. Second, the dyadic Green's function based on far-field-approximation will be applied to construct system response function of radar imaging problem. Third, a dual transform is further introduced to turn the imaging problem into the problem of physical-driven image processing problem. In this way, the image processing system response functions for all overlapping-patches will be identical for fixed transmitters and receivers. In other words, the system response functions of overlapping-patches are independent of specific imaging regions. So few system response functions matrices need be memorized. At last, the first-order method is introduced to implement the imaging process of all overlapping-patches in parallel. The far-field-approximation imaging method drastically decreases the memory requirement while maintaining fast imaging speed and high imaging quality. Therefore, the proposed fast imaging method based on far-field-approximation is applicable to more large-scale imaging scenarios. Some selected simulation results are presented to demonstrate the state-of-the-art performance of the far-field-approximation method

关键词： Imaging Radar imaging image processing Electromagnetics Electromagnetic scattering Computer science Millimeter wave technology

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