Chromosome image segmentation is an important step toward automatic karyotyping that involves visualization and interpretation of chromosomes. In this paper, we analyze the characteristics of chromosome images that ca...
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ISBN:
(纸本)0819425885
Chromosome image segmentation is an important step toward automatic karyotyping that involves visualization and interpretation of chromosomes. In this paper, we analyze the characteristics of chromosome images that can be effectively used for segmenting chromosomes and can be efficiently extracted on the Lockheed-Martin PAL parallelimage processor. We design and implement a parallel algorithm that uses local features to split touching chromosomes.
This paper describes a general methodology for developing parallelimageprocessing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applie...
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This paper describes a general methodology for developing parallelimageprocessing 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 parallelprocessing 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 imageprocessing that these slides are subject to is still limited both in terms of data processed and processingmethods. The work presented here focuses on the need to design and develop parallelimageprocessing 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 imageprocessingmethods 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.
A solution to the partitioning problem is presented for a class of data parallel algorithms (including for example explicit difference methods for time-dependent PDE, and imageprocessing algorithms based on local fil...
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A solution to the partitioning problem is presented for a class of data parallel algorithms (including for example explicit difference methods for time-dependent PDE, and imageprocessing algorithms based on local filters). Conditions are formulated, that characterize the optimal partitioning. From them, an explicit formula for the optimal partitioning is derived, which is valid in special cases. For the general case, the conditions provide a basis for the formulation of iterative partitioning algorithms. One such algorithm is proposed. The partitioning algorithm is intended as a tool to be used in utility routines or, ultimately, compilers, to enhance SPMD programming of MIMD-type computers with distributed memory. Results from an application in image analysis show that the algorithm is suitable for this purpose.
The assumption that the number of training samples is less than the number of pixels in a face image is essential for conventional eigenface-based face recognition. But recently, it has become impractical for massive ...
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The assumption that the number of training samples is less than the number of pixels in a face image is essential for conventional eigenface-based face recognition. But recently, it has become impractical for massive face image collections. A parallelprocessing method using distributed eigenfaces is presented. A massive face image set was divided into a bunch of small subsets that satisfied the assumption of conventional approaches. Eigenfaces were extracted from the subsets and stored in a cloud system. Face recognition was performed by parallelprocessing using the distributed eigenfaces in the cloud system. A face recognition system was implemented in the Hadoop system. Various experiments were performed to test the validity of the distributed eigenface-based approach. The experimental results show that, compared to conventional methods, the implemented distributed face recognition system worked well for large datasets without significant performance degradation.
This paper describes two different parallel computing approaches for imageprocessing problems on a Pentium based multiprocessor-system. These multiprocessor computers are often used as network servers. We demonstrate...
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ISBN:
(纸本)0819425885
This paper describes two different parallel computing approaches for imageprocessing problems on a Pentium based multiprocessor-system. These multiprocessor computers are often used as network servers. We demonstrate the utilization of one of these machines, equipped with four Intel Pentium processors, far a parallelimageprocessing task. A parallel computation of motion vector-fields based on correlation techniques is discussed to show the possible acceleration. The computational results show that a high efficiency can be reached, even a linear speedup is possible under certain conditions. Besides the mentioned correlation technique there are various imageprocessing problems that can easily be evaluated in parallel. Although massively parallel systems and special purpose systems are much faster, off-line imageprocessing can be accelerated by using these broadly available low-cost machines.
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. ...
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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.
The use of multiprocessor systems is a well suited solution to handle the problem of implementing realtime imageprocessing applications. We use a multiresolution image decomposition algorithm to show how the A(3) met...
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ISBN:
(纸本)0819425885
The use of multiprocessor systems is a well suited solution to handle the problem of implementing realtime imageprocessing applications. We use a multiresolution image decomposition algorithm to show how the A(3) methodology (Algorithm Architecture Adequation), and the CAD software SynDEx which support it, may improve the implementation of such algorithms on a multi-DSP architecture. The application algorithm as well as the hardware are specified with graphs, then the implementation may be formalized in terms of graphs transformations. This methodology reduces significantly the development cycle of imageprocessing applications, by simplifying test and debug process.
In this paper an efficient method devoted to estimate the velocity vectors field is investigated. The method is based on a quasi-interpolant operator and involves a large amount of computation. The operations characte...
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In this paper an efficient method devoted to estimate the velocity vectors field is investigated. The method is based on a quasi-interpolant operator and involves a large amount of computation. The operations characterizing the computational scheme are ideal for parallelprocessing because they are local, regular and repetitive. Therefore, the spatial parallelism of the process is studied to rapidly proceed in the computation on distributed multiprocessor systems. The process has shown to be synchoronous, with good task balancing and requiring a small amount of data transfer. (C) 2009 Elsevier Ltd. All rights reserved.
parallelprocessing of image analysis tasks is an essential method to speed up imageprocessing and helps to exploit the full capacity of distributed systems. However, writing parallel code is a difficult and time-con...
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ISBN:
(纸本)0819429074
parallelprocessing of image analysis tasks is an essential method to speed up imageprocessing and helps to exploit the full capacity of distributed systems. However, writing parallel code is a difficult and time-consuming process and often leads to an architecture-dependent program that has to be re-implemented when changing the hardware. Therefore it is highly desirable to do the parallelization automatically. For this we have developed a special kind of thread concept for image analysis tasks. Threads derivated from one subtask may share objects and run in the same context but may process different threads of execution and work on different data in parallel. In this paper we describe the basics of our thread concept and show how it can be used as basis of an automatic task parallelization to speed up imageprocessing. We further illustrate the design and implementation of an agent-based system that uses image analysis threads for generating and processingparallel programs by taking into account the available hardware. The tests made with our system prototype show that the thread concept combined with the agent paradigm is suitable to speed up imageprocessing by an automatic parallelization of image analysis tasks.
We present a new parallel volume rendering algorithm based on the split-light model for rendering and the Bulk Synchronous parallel (BSP) model for parallelization. The BSP model provides a simple and architecture-ind...
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ISBN:
(纸本)0819425885
We present a new parallel volume rendering algorithm based on the split-light model for rendering and the Bulk Synchronous parallel (BSP) model for parallelization. The BSP model provides a simple and architecture-independent approach to structure the parallel program. This parallel program has been tested on a shared memory SGI PowerChallenge machine, a distributed memory IBM SP2 machine and a network of UNIX workstations.
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