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SPIE Conference on parallel and distributed methods for image processing II

作者： Li, HZ Shi, HC Univ Missouri Dept Comp Engn & Comp Sci Columbia MO 65211 USA

ISBN: (纸本)0819429074

We propose a perspective volume graphics rendering algorithm on SIMD mesh-connected computers and implement the algorithm on the parallel Algebraic Logic (PAL) computer. The algorithm is a parallel ray casting algorithm. It decomposes the 3D perspective projection into two transformations that can be implemented in the SIMD fashion to solve the data redistribution problem caused by non-regular data access patterns in the perspective projection.

关键词： perspective volume rendering parallel algorithm mesh processor array

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parallel DATA RESAMPLING AND FOURIER INVERSION BY THE SCAN-LINE METHOD

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1995年第3期14卷 454-463页

作者： NOLL, DC WEBB, JA WARFEL, TE CARNEGIE MELLON UNIV SCH COMP SCIPITTSBURGHPA 15213 CARNEGIE MELLON UNIV DEPT ELECT & COMP ENGNPITTSBURGHPA 15213

Fourier inversion is an efficient method for image reconstruction in a variety of applications, for example, in computed tomography and magnetic resonance imaging. Fourier inversion normally consists of two steps, interpolation of data onto a rectilinear grid, if necessary, and inverse Fourier transformation, This paper presents interpolation by the scan-line method, in which the interpolation algorithm is implemented in a form consisting only of row operations and data transposes, The two-dimensional inverse Fourier transformation can also be implemented with only row operations and data transposes, Accordingly, Fourier inversion can easily be implemented on a parallel computer that supports row operations and data transposes on row distributed data The conditions under which the scan-line implementations are algorithmically equivalent to the original serial computer implementation are described and methods for improving accuracy outside of those conditions are presented, The scan-line algorithm is implemented on the iWarp parallel computer using the Adapt language for parallel image processing. This implementation is applied to magnetic resonance data acquired along radial-lines and spiral trajectories through Fourier transform space.

关键词： Resampling Interpolation parallel computers image reconstruction Fourier Interpolation Arithmetic Fourier Transform method of lines Inversion

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A Hierarchical distributed processing Framework for Big image Data

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IEEE Transactions on Big Data 2016年第4期2卷 297-309页

作者： Dong, Le Lin, Zhiyu Liang, Yan He, Ling Zhang, Ning Chen, Qi Cao, Xiaochun Izquierdo, Ebroul University of Electronic Science and Technology of China Chengdu611731 China China Academy of Engineering Physics Mianyang Sichuan621900 China Cyberspace Administration of the People's Republic of China Beijing100010 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100864 China Queen Mary University of London LondonE1 4NS United Kingdom

This paper introduces an effective processing framework nominated image Cloud processing (ICP) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e., Static ICP (SICP) and Dynamic ICP (DICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-image and Big-image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging imageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-The-Art methods, both in time efficiency and quality of results. © 2015 IEEE.

关键词： MapReduce

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parallel methods for similar image compression and classification with common models

Parallel methods for similar image compression and classific...

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SPIE Conference on parallel and distributed methods for image processing II

作者： Pianykh, OS Tyler, JM Sharman, R Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA

ISBN: (纸本)0819429074

This paper addresses efficient parallel compression and classification for sets of similar images that are normally generated from satellite imagery, medical imaging (CT and MR scans) or aerial surveillance. From our experiments it was observed that image similarities for each class of images can be more efficiently expressed in the domain of image compressing transforms. In particular, the paper shows that only one predictive compressing model can be constructed for the entire class of similar images of the same nature, and then used for nearly optimal compression of any image of the class. The extraction of the optimal class-compressing model still remains a computationally intensive process, which can be considerably improved on parallel computers. The paper demonstrates how a similar database compressing model can be extracted in parallel, and how this can be used for parallel similar database compression and classification of new images into appropriate similarity classes. The results of the parallel similar image analysis are demonstrated with MR and CT brain images obtained from the M.D. Anderson Cancer Center.

关键词： lossless image compression predictive models computer tomography (CT) magnetic resonance (MR)

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distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images

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DIAGNOSTIC PATHOLOGY 2011年第Suppl_sup期6卷 S16-S16页

作者： Zerbe, Norman Hufnagl, Peter Schluens, Karsten Charite Inst Pathol D-10117 Berlin Germany Univ Appl Sci Berlin D-12459 Berlin Germany

Background: Automated image analysis on virtual slides is evolving rapidly and will play an important role in the future of digital pathology. Due to the image size, the computational cost of processing whole slide images (WSIs) in full resolution is immense. Moreover, image analysis requires well focused images in high magnification. methods: We present a system that merges virtual microscopy techniques, open source image analysis software, and distributed parallel processing. We have integrated the parallel processing framework JPPF, so batch processing can be performed distributed and in parallel. All resulting meta data and image data are collected and merged. As an example the system is applied to the specific task of image sharpness assessment. imageJ is an open source image editing and processing framework developed at the NIH having a large user community that contributes image processing algorithms wrapped as plug-ins in a wide field of life science applications. We developed an imageJ plug-in that supports both basic interactive virtual microscope and batch processing functionality. For the application of sharpness inspection we employ an approach with non-overlapping tiles. Compute nodes retrieve image tiles of moderate size from the streaming server and compute the focus measure. Each tile is divided into small sub images to calculate an edge based sharpness criterion which is used for classification. The results are aggregated in a sharpness map. Results: Based on the system we calculate a sharpness measure and classify virtual slides into one of the following categories - excellent, okay, review and defective. Generating a scaled sharpness map enables the user to evaluate sharpness of WSIs and shows overall quality at a glance thus reducing tedious assessment work. Conclusions: Using sharpness assessment as an example, the introduced system can be used to process, analyze and parallelize analysis of whole slide images based on open source software.

关键词： Virtual Slide Focus Measure Virtual Microscopy Stream Server Slide image

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Large-scale grid computing for content-based image retrieval

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ASLIB PROCEEDINGS 2010年第4-5期62卷 438-446页

作者： Town, Chris Harrison, Karl Univ Cambridge Comp Lab Cambridge CB2 3QG England Imense Ltd Cambridge England Univ Birmingham Sch Phys & Astron Birmingham W Midlands England

Purpose - Content-based image retrieval (CBIR) technologies offer many advantages over purely text-based image search. However, one of the drawbacks associated with CBIR is the increased computational cost arising from tasks such as image processing, feature extraction, image classification, and object detection and recognition. Consequently CBIR systems have suffered from a lack of scalability, which has greatly hampered their adoption for real-world public and commercial image search. At the same time, paradigms for large-scale heterogeneous distributed computing such as grid computing, cloud computing, and utility-based computing are gaining traction as a way of providing more scalable and efficient solutions to large-scale computing tasks. Design/methodology/approach - This paper presents an approach in which a large distributed processing grid has been used to apply a range of CBIR methods to a substantial number of images. By massively distributing the required computational task across thousands of grid nodes, very high through-put has been achieved at relatively low overheads. Findings - This has allowed one to analyse and index about 25 million high resolution images thus far, while using just two servers for storage and job submission. The CBIR system was developed by Imense Ltd and is based on automated analysis and recognition of image content using a semantic ontology. It features a range of image-processing and analysis modules, including image segmentation, region classification, scene analysis, object detection, and face recognition methods. Originality/value - In the case of content-based image analysis, the primary performance criterion is the overall through-put achieved by the system in terms of the number of images that can be processed over a given time frame, irrespective of the time taken to process any given image. As such, grid processing has great potential for massively parallel content-based image retrieval and other tasks with similar p

关键词： Data handling Pattern recognition Data analysis Virtual work

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Block rotation: Implementation and applications

Block rotation: Implementation and applications

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Conference on parallel and distributed methods for image processing

作者： Fatemi, O Panchanathan, S Univ. of Ottawa (Canada) Univ. of Ottawa (United States)

ISBN: (纸本)0819425885

Visual media processing is becoming increasingly important because of the wide variety of image and video based applications. Block rotation is an important operation in different image/video processing tasks such as graphics, fractal processing, pattern matching and image registration. Remote sensing, medical imaging, computer vision, computer graphics, and video coding are typical applications of digital image rotation. However, a hardware implementation of the block rotation algorithm has not been realized and software implementation is slow. Hence, they are not suitable for real-time execution. In this paper, we propose a novel method for block rotation, which is fast and suitable for hardware implementation. The algorithm employs area based interpolation. Experimental results have shown the performance enhancement compared to classical interpolation algorithms at a similar level of complexity.

关键词： image rotation spatial transformation affine transform interpolation forward mapping and backward mapping

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parallel approaches to machine learning - A comprehensive survey

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JOURNAL OF parallel AND distributed COMPUTING 2013年第3期73卷 284-292页

作者： Upadhyaya, Sujatha R. Infosys Technol Bangalore Karnataka India

Literature has always witnessed efforts that make use of parallel algorithms / parallel architecture to improve performance;machine learning space is no exception. In fact, a considerable effort has gone into this area in the past fifteen years. Our report attempts to bring together and consolidate such attempts. It tracks the development in this area since the inception of the idea in 1995, identifies different phases during the time period 1995-2011 and marks important achievements. When it comes to performance enhancement, GPU platforms have carved a special niche for themselves. The strength of these platforms comes from the capability to speed up computations exponentially by way of parallel architecture / programming methods. While it is evident that computationally complex processes like image processing, gaming etc. stand to gain much from parallel architectures;studies suggest that general purpose tasks such as machine learning, graph traversal, and finite state machines are also identified as the parallel applications of the future. Map reduce is another important technique that has evolved during this period and as the literature has it, it has been proved to be an important aid in delivering performance of machine learning algorithms on GPUs. The report summarily presents the path of developments. (C) 2012 Elsevier Inc. All rights reserved.

关键词： distributed and parallel machine learning GPU Map reduce

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Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised image Segmentation

Inverse Quantum Fourier Transform Inspired Algorithm for Uns...

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37th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Akinola, Taoreed Li, Xiangfang Wilkins, Richard Obiomon, Pamela Qian, Lijun Prairie View A&M Univ Dept Elect & Comp Engn Prairie View TX 77446 USA

ISBN: (纸本)9798350311990

image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels' intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsuthresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).

关键词： Inverse Quantum Fourier Transform Computer Vision image Segmentation

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Interlayer communication mechanisms for a heterogeneous image processing system

Interlayer communication mechanisms for a heterogeneous imag...

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Conference on parallel and distributed methods for image processing

作者： Lau, WC Fung, FY Ercan, MF Hong Kong Polytechnic Univ. (Hong Kong)

ISBN: (纸本)0819425885

A heterogeneous system for image processing was designed. The system consists of two layers with an array of DSPs in the low level layer and an array of PCs in the high level layer. The system resembles two stage multi-layers architecture. In the preprocessing phase, image data is delivered to the low level processors and operations are handled by using SPMD paradigm. The processed data is delivered to the high level processors where feature extraction and recognition tasks are performed. Apparently, the performance of this system is highly depended on communication mechanism used. The communication between two heterogeneous processing layers is a critical design issue and will be described in this paper. In our design, we have employed QuickRing. QuickRing provides a high transfer rate, 160Mbytes/s, and supports Multicast mode of data transmission that is suitable for broadcasting of data.

关键词： QuickRing heterogeneous Multicast performance analysis

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