Membrane computing is a novel class of distributedparallel computing models. Application of membrane computing to imageprocessing is an interesting topic. In this paper, we propose a thresholding method based on P s...
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Existing cloud frameworks involve isolating low-level operations within an application for data distribution and partitioning. This limits their applicability for many applications with complex data dependency conside...
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Existing cloud frameworks involve isolating low-level operations within an application for data distribution and partitioning. This limits their applicability for many applications with complex data dependency considerations. This paper aims to explore new methods of partitioning and distributing data in the cloud by fundamentally re-thinking the way in which future data management models will need to be developed on the Internet. Loosely-coupled associative computing techniques, which have so far not been considered, can provide the break-through needed for a distributed data management scheme. Using a novel lightweight associative memory algorithm known as Edge Detecting Hierarchical Graph Neuron (Edge HGN), data retrieval/processing can be modeled as a pattern recognition problem, conducted across multiple records within a single-cycle, utilizing a parallel approach. The proposed model envisions a distributed data management scheme for large-scale data processing and database updating that is capable of providing scalable real-time recognition and processing with high accuracy while being able to maintain low computational cost in its function.
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 a...
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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.
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 o...
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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 imageprocessing, 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.
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 ...
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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。
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 im...
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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 distributedparallelprocessing. We have integrated the parallelprocessing 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 imageprocessing 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.
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 b...
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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.
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 p...
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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.
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 approxima...
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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.
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