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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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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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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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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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Efficient image matching: a hierarchical chamfer matching scheme via distributed system

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Real-time imaging 1995年第4期1卷 [d]245-259页

作者： You, J. Pissaloux, E. Zhu, W.P. Cohen, H.A. Univ of South Australia

image matching has played a key role in object recognition and localization. One central problem is to find an efficient and effective approach to search for the best matching between two image sets. In contrast to the conventional matching techniques, the innovation of our method detailed in this paper is to propose a hierarchical Chamfer matching scheme based on the dynamic detection of interesting points. The algorithm extends the traditional methods by introducing interesting points to replace edge points in distance transform for the matching measurement. The search for the best matching is guided by minimizing a given matching criterion in an interesting points pyramid from coarse level to fine level. The pyramid is created through a dynamic thresholding scheme and such a hierarchical structure aims to reduce the computation load. The processing speed is further improved by parallel implementation on a low cost heterogeneous PVM (parallel Virtual Machine) network without specific software and hardware requirements. The experimental results demonstrate that our algorithm is simple to implement and quite insensitive to noise and other disturbances with reliability and efficiency.

关键词： image processing

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oRis: Multiagents approach for image processing

oRis: Multiagents approach for image processing

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SPIE Conference on parallel and distributed methods for image processing II

作者： Rodin, V Harrouet, F Ballet, P Tisseau, J Technopole Brest Iroise Ecole Natl Ingn Brest F-29608 Brest France

ISBN: (纸本)0819429074

In this article, we present a parallel image processing system based on the concept of reactive agents. This means that, in our system, each agent has a very simple behavior which allows it to take a decision (find out an edge, a region,...) according to its position in the image and to the information enclosed in it. Our system lies in the oRis language, which allows to describe very finely and simply the agents' behaviors. In fact, oRis is an interpreted and dynamic multiagent language. First of all, oRis is an object language with the use of classes regrouping attributes and methods. The syntax is close to the CSS language and includes notions of multiple inheritance, oRis is also an agent language: every object with a method 'main()' becomes an agent. This method is cyclically executed by the system scheduler and corresponds to the agent behavior. We also present an application made with oRis. This application allows to detect concentric striae located on different natural "objects" (age-rings of tree, fish otolith growth rings, striae of some minerals,...). The stopping of the multiagent system is implemented through a technique issued from immunology: the apoptosis.

关键词： multiagent language multiagent system parallel image processing reactive agents striae detection

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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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Contract-Linda: A paradigm for programming heterogeneous parallel systems

Contract-Linda: A paradigm for programming heterogeneous par...

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Conference on parallel and distributed methods for image processing

作者： Pycock, D Jackson, RO Univ. of Birmingham (United Kingdom)

ISBN: (纸本)0819425885

Contract-Linda is a novel programming architecture for heterogeneous parallel machines particularly suited to image processing. Previous research has concentrated on static and pre-determined scheduling of computation and on fine grain parallelism. Pre-determined scheduling is satisfactory in cases where the computational process is fully deterministic. However with many image interpretation schemes the flow of control and the nature of the computational procedures can only be determined at run-time. In this paper we describe a programming paradigm for coarse grain and task level parallelism. Task management is based on the Contract Net protocol and utilises the Linda. Coordination Language to provide run-time scheduling. This paradigm accommodates a closely coupled network of heterogeneous processing modules which differ greatly in computational capability;modules based on neural networks, semantic networks, vector and scalar processors are accommodated. Contract;Linda allo-cvs specialised heterogeneous machines to be exploited using a straightforward generic programming model. It does this by providing an internal task management mechanism which ensures that the heterogeneous processing elements are used by the tasks most suited to them and exploits dynamic parallelism within the problem as it is solved. By separating the task of describing the problem from that of describing how the work is carried out on the machine (and providing a solution for this problem) we allow applications to be quickly developed which can effectively utilise specialised machines without the need for specialised programming. We report an experiment to re-implement a cell image interpretation system using Contract-Linda.

关键词： heterogeneous computing parallel computing distributed artificial intelligence image interpretation

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Kulla, a container-centric construction model for building infrastructure-agnostic distributed and parallel applications

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JOURNAL OF SYSTEMS AND SOFTWARE 2020年 168卷 110665-110665页

作者： Reyes-Anastacio, Hugo G. Gonzalez-Compean, J. L. Sosa-Sosa, Victor J. Carretero, Jesus Garcia-Blas, Javier CINVESTAV Unidad Tamaulipas Km 5-5 Carr Soto la Marina Victoria 87130 Tamaulipas Mexico Univ Carlos III Madrid Comp Sci & Engn Dept Av Univ 30 Madrid 28911 Spain

This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastructure-agnostic distributed and parallel applications. In Kulla, applications, dependencies and environment settings, are mapped with construction units called Kulla-Blocks. A parallel programming model enables developers to couple those interoperable structures for creating constructive structures named Kulla-Bricks. In these structures, continuous dataflow and parallel patterns can be created without modifying the code of applications. methods such as Divide&Containerize (data parallelism), Pipe&Blocks (streaming), and Manager/Block (task parallelism) were developed to create Kulla-Bricks. Recursive combinations of Kulla instances can be grouped in deployment structures called Kulla-Boxes, which are encapsulated into VCs to create infrastructure-agnostic parallel and/or distributed applications. Deployment strategies were created for Kulla-Boxes to improve the IT resource profitability. To show the feasibility and flexibility of this model, solutions combining real-world applications were implemented by using Kulla instances to compose parallel and/or distributed system deployed on different IT infrastructures. An experimental evaluation based on use cases solving satellite and medical image processing problems revealed the efficiency of Kulla model in comparison with some traditional state-of-the-art solutions. (C) 2020 Published by Elsevier Inc.

关键词： Infrastructure-agnostic applications Construction model Virtual containers parallel patterns Pipelines

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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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