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image-ANALYSIS USING MULTIGRID RELAXATION methods

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1986年第2期8卷 129-139页

作者： TERZOPOULOS, D M.I.T Artificial Intelligence Laboratory 54 Technology Square Cambridge MA 02139

image analysis problems, posed mathematically as variational principles or as partial differential equations, are amenable to numerical solution by relaxation algorithms that are local, iterative, and often parallel. Although they are well suited structurally for implementation on massively parallel, locally interconnected computational architectures, such distributed algorithms are seriously handi capped by an inherent inefficiency at propagating constraints between widely separated processing elements. Hence, they converge extremely slowly when confronted by the large representations of early vision. Application of multigrid methods can overcome this drawback, as we showed in previous work on 3-D surface reconstruction. In this paper, we develop multiresolution iterative algorithms for computing lightness, shape-from-shading, and optical flow, and we examine the efficiency of these algorithms using synthetic image inputs. The multigrid methodology that we describe is broadly applicable in early vision. Notably, it is an appealing strategy to use in conjunction with regularization analysis for the efficient solution of a wide range of ill-posed image analysis problems.

关键词： image analysis Relaxation methods Iterative algorithms Partial differential equations Concurrent computing distributed computing Computer architecture distributed algorithms Optical propagation Ultraviolet sources Author Keywords variational principles image analysis inverse problems lightness multigrid relaxation multiresolution algorithms optical flow parallel algorithms partial differential equations shape from shading

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A parallel solution for high resolution histological image analysis

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COMPUTER methods AND PROGRAMS IN BIOMEDICINE 2012年第1期108卷 388-401页

作者： Bueno, G. Gonzalez, R. Deniz, O. Garcia-Rojo, M. Gonzalez-Garcia, J. Fernandez-Carrobles, M. M. Vallez, N. Salido, J. Univ Castilla La Mancha VISILAB ETS Ingenieros Ind E-13071 Ciudad Real Spain Hosp Gen Univ Ciudad Real Dpt Anat Patol Ciudad Real Spain

This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories. (c) 2012 Elsevier Ireland Ltd. All rights reserved.

关键词： Low and high-level parallel image processing High resolution histological image processing MPI Microscopy Whole slide imaging

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distributed parallel Super-Block-Based Star Detection and Centroid Calculation

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IEEE SENSORS JOURNAL 2018年第19期18卷 8096-8107页

作者： Wang, Zhen Jiang, Jie Zhang, Guangjun Beijing Univ Aeronaut & Astronaut Sch Instrumentat Sci & Optoelect Engn Minist Educ Key Lab Precis Optomechatron Technol Beijing 100191 Peoples R China

Due to the inadequacies of the speed of star detection and centroid calculation (SDCC), the applications of high-resolution and high-frame-rate image sensors are greatly limited in star trackers, restricting the performance of the star trackers. To resolve this problem, a novel distributed parallel super-block-based SDCC method is proposed in this paper. In contrast to the previous SDCC methods, the proposed method divides the star images into several sub-images and processes these sub-images in parallel. Then, by using a super-block-based method and a distributed shared memory-based method to process the stars in sub-images (sub-stars) and in adjacent sub-images (boundary stars), respectively, the position information of all the stars are detected during the parallel scan of the sub-images. The proposed method exhibits a speed that is up to 4 x M times faster than the previous SDCC methods in processing the star images that are divided into M sub-images. Experimental results demonstrate that the proposed method is correct and effective. It is applicable to the use of most high-performance image sensors and resolves the performance limitation of star trackers to achieve better attitude accuracy and attitude update rate.

关键词： distributed parallel star detection centroid location star tracker distributed shared memory image sensors application

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Theoretical Analysis of an Adaptive Periodic Multi Installment Scheduling With Result Retrieval for SAR image processing

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第12期33卷 4672-4683页

作者： Chinnappan, Gokul Madathupalyam Veeravalli, Bharadwaj Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore

processing a large-scale Synthetic Aperture Radar (SAR) image dataset on a distributed computing infrastructure poses a challenging problem. Large-scale load distribution strategies like multi-installment scheduling (MIS) assume that the size of the result is negligible compared to the input workloads and hence ignore it in their design. Similarly, numerical methods like particle swarm optimization and their variants are not practical for real-time applications, given their run-time complexities. As both the results retrieval and completion time are crucial for SAR image data processing, in this article, we attempt to provide a thorough theoretical analysis of an adaptive MIS that includes the result retrieval phase. We use the periodic nature of the internal installments to keep the strategy simple and fine-tune the last installment to avoid any idle times in the processors. We derive a closed-form solution for the load fractions and hence, the overall processing time, schedule feasibility criteria, and certain other properties that lead to adaptive scheduling. Finally, we validate our theoretical findings through rigorous simulation studies using a loosely connected virtual machines (VMs) topology for the SAR dataset.

关键词： Program processors Radar polarimetry Processor scheduling Load modeling distributed databases Computational modeling Schedules SAR image multi-installment scheduling load distribution front-end processors heterogeneous cluster

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A toolkit for parallel image processing

A toolkit for parallel image processing

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SPIE Conference on parallel and distributed methods for image processing II

作者： Squyres, JM Lumsdaine, A Stevenson, RL Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)0819429074

In this paper, we present the design and implementation of a parallel image processing software library (the parallel image processing Toolkit). The Toolkit not only supplies a rich set of image processing routines, it is designed principally as an extensible framework containing generalized parallel computational kernels to support image processing. Users can easily add their own image processing routines without knowledge or explicit use of the underlying data distribution mechanisms or parallel computing model. Shared memory and multi-level memory hierarchies are exploited to achieve high performance on each node, thereby minimizing overall parallel execution time. Multiple load balancing schemes have been implemented within the parallel framework that transparently distribute the computational load evenly on a distributed memory computing environment. Inside the Toolkit, a message-passing model of parallelism is designed around the Message Passing Interface (MPI) standard. Experimental results are presented to demonstrate the parallel speedup obtained with the parallel image processing Toolkit in a typical workstation cluster with some common image processing tasks.

关键词： parallel image toolkit library MPI processing extensible load-balancing

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Load balancing methods for ray tracing and binary tree computing using PVM

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parallel COMPUTING 1995年第12期21卷 1963-1978页

作者： Sekharan, CN Goel, V Sridhar, R UNIV CENT FLORIDA DEPT COMP SCIORLANDOFL 32816 UNIV OKLAHOMA SCH COMP SCINORMANOK 73019

We propose efficient load balancing methods for two computational problems namely ray tracing and bottom-up binary tree computing in a distributed environment. In the context of ray tracing, we propose a variant of a static load balancing technique presented in [15] where the sampling is based on partitioning the object space. Our approach partitions the image instead and uses an efficient scheduling technique for load balancing. Computations carried out on a binary tree arise naturally in image processing and network optimization problems. Many of these problems are solved efficiently in parallel by the popular tree contraction technique [1]. In this paper, we explore the tree-contraction technique in a distributed setting using the grain packing method [9]. Implementations of our algorithms on a cluster of workstations using parallel Virtual Machine (PVM) [6] demonstrate near-perfect load balancing.

关键词： load balancing ray tracing binary tree distributed environment PVM workstation cluster

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An identification framework for print-scan books in a large database

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INFORMATION SCIENCES 2017年 396卷 33-54页

作者： Lee, Sang-Hoon Kim, Jongyoo Lee, Sanghoon Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea

In this paper, we propose an identification framework to determine copyright infringement in the form of illegally distributed print-scan books in a large database. The framework contains following main stages: image pre-processing, feature vector extraction, clustering, and indexing, and hierarchical search. The image pre-processing stage provides methods for alleviating the distortions induced by a scanner or digital camera. From the preprocessed image, we propose to generate feature vectors that are robust against distortion. To enhance the clustering performance in a large database, we use a clustering method based on the parallel-distributed computing of Hadobp MapReduce. In addition, to store the clustered feature vectors efficiently and minimize the searching time, we investigate an inverted index for fedture vectors. Finally, we implement a two-step hierarchical search to achieve fast and accurate on-line identification. In a simulation, the proposed identification framework shows accurate and robust in the presence of print-scan distortions. The processing time analysis in a parallel computing environment gives extensibility of the proposed framework to massive data. In the matching performance analysis, we empirically and theoretically find that in terms of query time, the optimal number of clusters scales with O(root N) for N print-scan books. (C) 2017 Elsevier Inc. All rights reserved.

关键词： image identification image databases parallel databases MapReduce Inverted index Hierarchical search

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Software platform for parallel image processing and computer vision

Software platform for parallel image processing and computer...

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Conference on parallel and distributed methods for image processing

作者： Taniguchi, R Makiyama, Y Tsuruta, N Yonemoto, S Arita, D Kyushu Univ. (Japan)

ISBN: (纸本)0819425885

We describe a general purpose environment for the development of parallel image processing/computer vision algorithms: PRIME (parallel image Media processing Environment). ''General purpose'' here means that the environment is designed so as to be used on a variety of multi-processor systems ranging from tightly-coupled computers to loosely-coupled computers. The key point of the system is that it provides an architecture-independent programming environment for image processing and computer vision. We show the outline of PRIME, its implementation, and its preliminary performance evaluation.

关键词： parallel programming image processing computer vision architecture-independence MPI

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A fast and fully distributed method for region-based image segmentation Fast distributed region-based image segmentation

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JOURNAL OF REAL-TIME image processing 2021年第3期18卷 793-806页

作者： Mazouzi, Smaine Guessoum, Zahia Skikda Univ 20 Aout 1955 Comp Sci Dept Skikda Algeria Univ Reims Sorbonne Univ CReSTIC Reims Lip6 Paris France

distributed and parallel computing techniques allow fast image processing, namely when these techniques are applied at the low and the medium level of a vision system. In this paper, a collective and distributed method for image segmentation is introduced and evaluated. The method is modeled as a multi-agent system, where the agents aim to collectively produce a region-based segmentation. Each agent starts searching for an acceptable region seed by randomly jumping within the image. Next, it performs a region growing around its position. Thus, several agents find themselves within the same homogeneous region and are organized in a graph where two agents are connected if they are within the same region. So, a unifying of the labels in a same region is collaboratively performed by the agents themselves. The proposed method was experimented on real range images from the ABW dataset and the Object Segmentation Database (OSD) one, and the obtained results were compared to those of some well-referenced methods from the literature. The evaluation results show that the proposed method provides fast and accurate image segmentation, allowing it to be deployed for real-time vision systems.

关键词： image segmentation Region growing Pixel labeling distributed computing Real-time computer vision systems

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A Multi-granularity parallel Model for Unified Remote Sensing image processing WebServices

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TRANSACTIONS IN GIS 2012年第6期16卷 845-866页

作者： Guo, Wei Zhu, Xinyan Hu, Tao Fan, LiWei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R LIESMARS Wuhan 430079 Peoples R China

The growth of the Web has resulted in the Web-based sharing of distributed geospatial data and computational resources. The Geospatial processing Web (GeoPW) described here is a set of services that provide a wide array of geo-processing utilities over the Web and make geo-processing functionalities easily accessible to users. High-performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high-performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi-granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI-oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub-models. In addition, interfaces, definitions, parallel task scheduling and fault-tolerance mechanisms are described in detail. Based on the model and methods, we propose an open-source online platform named OpenRS-Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi-granularity parallel model for unified remote sensing image processing web services.

关键词： REMOTE-sensing images image processing WEB services parallel programs (Computer programs) GEOSPATIAL data GEOGRAPHIC information systems

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