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IEEE Workshop on Multimedia Signal processing

作者： Lin Sun Oscar C. Au Wei Dai Ruobing Zou Sijin Li Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

Adaptive quantization matrix design for different block sizes is one of the possible methods to improve the RD performance in video coding and has recently attracted the focus of many researchers. In this paper, we first analyze the shortcomings of the evenly distributed distortion method which was proposed recently. In order to tackle these problems, we propose two modified methods, method I with relaxed distortion constraints and method ii is iterative boundary distortion minimization problem considering variance adaptively. Both problems can be solved using convex optimization effectively and efficiently. Simulations have been conducted based on HM4.0, which is the reference software of the latest High Efficiency Video Coding (HEVC). Simulation results show the effect of our proposed methods. Both methods show their significance when evaluated by RD performance.

关键词： Standards Quantization Discrete cosine transforms Video coding Convex functions Frequency domain analysis image coding

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Evaluation and comparison of cell nuclei detection algorithms

Evaluation and comparison of cell nuclei detection algorithm...

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IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Sándor Szénási Zoltán Vámossy Miklós Kozlovszky Doctoral School of Applied Informatics Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Laboratory of Parallel and Distributed Computing MTA SZTAKI Budapest Hungary

The processing of microscopic tissue images and especially the detection of cell nuclei is nowadays done more and more using digital imagery and special immunodiagnostic software products. Since several methods (and applications) were developed for the same purpose, it is important to have a measuring number to determine which one is more efficient than the others. The purpose of the article is to develop a generally usable measurement number that is based on the “gold standard” tests used in the field of medicine and that can be used to perform an evaluation using any of image segmentation algorithms. Since interpreting the results themselves can be a pretty time consuming task, the article also contains a recommendation for the efficient implementation and a simple example to compare three algorithms used for cell nuclei detection.

关键词： Accuracy Algorithm design and analysis image segmentation Time measurement Gold Standards Classification algorithms

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基于分布式多图像处理器的医学影像体绘制算法

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中国医学装备 2012年第8期9卷 4-7页

作者：商洪涛唐辉徐涛北京军区总医院医学工程科北京100700

目的:计算机医学图像处理具有所需计算量和计算涉及数据量巨大的特点,需将高性能计算技术应用于计算机医学图像处理中,以适应计算涉及数据量大和计算速度快的要求。方法:计算统一设备架构(CUDA)技术可使图像处理单元(GPU)进行通用并行计... 详细信息

目的:计算机医学图像处理具有所需计算量和计算涉及数据量巨大的特点,需将高性能计算技术应用于计算机医学图像处理中,以适应计算涉及数据量大和计算速度快的要求。方法:计算统一设备架构(CUDA)技术可使图像处理单元(GPU)进行通用并行计算,使其能够提供强大的计算能力。由于分布式并行计算是实现高性能计算的主要方式,故提出一种实用的分布式并行计算模式,即基于分布式多GPU的并行计算模式,其中多台计算机将使用消息传递接口(MPI)并行编程环境配置成分布式集群系统。结果:以医学影像体绘制算法为例分析该计算模式,使得其算法计算时间较短,计算效率较高。结论:使用MPI集群和GPU进行算法加速,可很好地解决无法实现医学影像的实时三维重建的问题。

关键词：分布式多图像处理单元光线投射体绘制医学影像处理算法

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New Divisible Load Distribution methods using Pipelined Communication Techniques on Tree and Pyramid Networks

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2011年第2期47卷 806-819页

作者： Li, Keqin SUNY Coll New Paltz New Paltz NY 12561 USA

A divisible load can be arbitrarily divided into independent small load fractions which are assigned to processors in a parallel or distributed computing system for simultaneous processing. The theory and techniques of divisible load distribution have a wide range of aerospace applications, including satellite signal and image processing, radar and infrared tracking, target identification and searching, and data reporting and aggregation and processing in wireless sensor networks. We make new progress on divisible load distribution on tree and pyramid networks. We revisit the classic method for divisible load distribution on partitionable static interconnection networks (including complete tree and pyramid networks) and derive a closed-form expression of the parallel time and speedup. We propose two new methods which employ pipelined communication techniques to distribute divisible loads on tree and pyramid networks. We derive closed-form expressions of the parallel time and speedup for both methods and show that the asymptotic speedup of both methods is b beta + 1 for a complete b-ary tree network and 4 beta + 1 for a pyramid network, where beta is the ratio of the time for computing a unit load to the time for communicating a unit load. The technique of pipelined communications leads to improved performance of divisible load distribution on tree and pyramid networks. Compared with the classic method, the asymptotic speedup of our new methods is 100% faster on a complete binary tree network and 33% faster on a pyramid network for large beta.

关键词： LOADS (Mechanics) SENSOR networks COMPUTER systems AEROSPACE engineering image processing TARGET acquisition WIRELESS sensor networks PIPELINING (Electronics)

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云环境中基于金字塔模型的影像数据存储方法

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重庆邮电大学学报（自然科学版） 2012年第6期24卷 669-674页

作者：夏英杨选伦重庆邮电大学计算机科学与技术学院重庆400065

随着卫星遥感获取技术的发展,影像数据量呈几何式增长,传统数据存储技术已经无法适应海量影像数据的处理要求。结合金字塔模型和MapReduce架构提出了一种适用于云计算环境的分布式并行存储方法—BMLStorage(storage based on MapReduce ... 详细信息

随着卫星遥感获取技术的发展,影像数据量呈几何式增长,传统数据存储技术已经无法适应海量影像数据的处理要求。结合金字塔模型和MapReduce架构提出了一种适用于云计算环境的分布式并行存储方法—BMLStorage(storage based on MapReduce and local file system),该方法基于金字塔模型对影像数据进行分层分块处理,并对所得瓦片重新编码。定义了一种新的存储规则,使得所有瓦片可以按照此规则利用Hadoop中的MapReduce框架实现并行存储。实验表明,该方法与现有方法相比,在海量影像数据存储性能方面有一定的提高。

关键词：云计算 MapReduce 金字塔模型影像数据

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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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parallel Implementation of Edge-Directed image Interpolation on a Graphics processing Unit

Parallel Implementation of Edge-Directed Image Interpolation...

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17th IEEE International Conference on parallel and distributed Systems (ICPADS)

作者： Wu, Jiaji Li, Tao Huang, Bormin Xidian Univ Sch Elect Engn Xian Peoples R China Univ Wisconsin Space Sci & Engn Ctr Madison WI USA

ISBN: (纸本)9780769545769

The edge-directed interpolation scheme is a noniterative, orientation-adaptive method to enhance image resolution with better visual effect than conventional interpolation methods. It interpolates the missing pixels based on the covariance of a high-resolution image estimated from the covariance of the low-resolution image. In spite of the impressive performance, the computational complexity of covariance-based adaptation is significantly higher than that of the conventional linear interpolation algorithms. In this paper, we propose a GPU-based massively parallel version of the edge-directed interpolation scheme. A speedup of 61.7x can be achieved with respect to its single-threaded CPU counterpart in the host computer.

关键词： edge-directed interpolation Graphics processing Unit (GPU) CUDA parallel computing

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parallel fuzzy connected image segmentation on GPU

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MEDICAL PHYSICS 2011年第7期38卷 4365-4371页

作者： Ying Zhuge Yong Cao Udupa, Jayaram K. Miller, Robert W. NCI Radiat Oncol Branch NIH Bethesda MD 20892 USA Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24060 USA Univ Penn Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA

Purpose: image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3599725]

关键词： image segmentation fuzzy connectedness fuzzy affinity GPU

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parallel Matrix Algorithms for image processing

Parallel Matrix Algorithms for Image Processing

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2nd International Conference on parallel, distributed, Grid and Cloud Computing for Engineering

作者： Kotas, P. Vondrak, V. Praks, P. Stachon, M. VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Appl Math Ostrava Czech Republic

ISBN: (纸本)9781905088423

The aim of this work is to solve the image retrieval problems with modern methods of numerical linear algebra, which can be easily parallelized for distributed memory architectures like a cluster platform. Algorithm presented in this paper is singular value decomposition (SVD). We show that SVD is directly linked with information retrieval through latent semantic indexing. However, our main concern is efficiency of computations of SVD. We present our parallel implementation of Householder bidiagonalization, which we consider the most computationally demanding step of singular value decomposition. We shall also compare our proposed algorithms with commonly used approaches on the experiments, and we shall emphasize their advatages in sence of usage of standard optimized linear algebra packages.

关键词： singular value decomposition latent semantic indexing matrix algorithms image retrieval

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parallel and distributed vision algorithms using dual decomposition

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COMPUTER VISION AND image UNDERSTANDING 2011年第12期115卷 1721-1732页

作者： Strandmark, Petter Kahl, Fredrik Schoenemann, Thomas Lund Univ Ctr Math Sci S-22100 Lund Sweden

We investigate dual decomposition approaches for optimization problems arising in low-level vision. Dual decomposition can be used to parallelize existing algorithms, reduce memory requirements and to obtain approximate solutions of hard problems. An extensive set of experiments are performed for a variety of application problems including graph cut segmentation, curvature regularization and more generally the optimization of MRFs. We demonstrate that the technique can be useful for desktop computers, graphical processing units and supercomputer clusters. To facilitate further research, an implementation of the decomposition methods is made publicly available. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Graph cuts Dual decomposition parallel MRF MPI GPU

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