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 ...
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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 imageprocessing 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.
We describe a distributed and iterative approach to perform the unitary transformations in the square root information filter implementation of the Kalman filter, providing an alternative to the common QR factorizatio...
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We describe a distributed and iterative approach to perform the unitary transformations in the square root information filter implementation of the Kalman filter, providing an alternative to the common QR factorization-based approaches. The new approach is useful in approximate computation of filtered estimates for temporally evolving random fields defined by local interactions and observations. Using several examples motivated by computer vision applications, we demonstrate that near-optimal estimates can be computed for problems of practical importance using only a small number of iterations, which can be performed in a finely parallel manner over the spatial domain of the random field.
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 th...
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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.
Fourier inversion is an efficient method for image reconstruction in a variety of applications, for example, in computed tomography and magnetic resonance imaging. Fourier inversion normally consists of two steps, int...
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Fourier inversion is an efficient method for image reconstruction in a variety of applications, for example, in computed tomography and magnetic resonance imaging. Fourier inversion normally consists of two steps, interpolation of data onto a rectilinear grid, if necessary, and inverse Fourier transformation, This paper presents interpolation by the scan-line method, in which the interpolation algorithm is implemented in a form consisting only of row operations and data transposes, The two-dimensional inverse Fourier transformation can also be implemented with only row operations and data transposes, Accordingly, Fourier inversion can easily be implemented on a parallel computer that supports row operations and data transposes on row distributed data The conditions under which the scan-line implementations are algorithmically equivalent to the original serial computer implementation are described and methods for improving accuracy outside of those conditions are presented, The scan-line algorithm is implemented on the iWarp parallel computer using the Adapt language for parallelimageprocessing. This implementation is applied to magnetic resonance data acquired along radial-lines and spiral trajectories through Fourier transform space.
This paper presents a parallel approach to a hierarchical image matching scheme using the Hausdorff distance for object recognition and localization in aerial images. Unlike the conventional matching methods in which ...
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This paper presents a parallel approach to a hierarchical image matching scheme using the Hausdorff distance for object recognition and localization in aerial images. Unlike the conventional matching methods in which edge pixels are considered as image feature pixels in distance transform and the blind pointwise comparison procedure in terms of the Hausdorff distance is applied, a guided image matching system is developed by the hierarchical detection of interesting points via a dynamic thresholding scheme for the search of the best matching between two image sets. Furthermore, the concept of remote procedure call (RPC) in distributed systems is introduced for the parallel implementation to achieve the speedup without specific software and hardware requirement.
Among other adaptive wavelet analysis methods, wavelet packet best basis selection has become a popular method in image compression. This paper introduces a subband based parallelization of the decomposition and the b...
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Many avionics systems use specialized parallel architectures to speed processing and to increase system reliability. The software used therein is frequently divided into tasks and executed concurrently on multiple pro...
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Many avionics systems use specialized parallel architectures to speed processing and to increase system reliability. The software used therein is frequently divided into tasks and executed concurrently on multiple processors under strict real-time constraints critical to the mission's successful performance. Scheduling and planning are needed for effectively managing the computational resources on such avionics architectures. Since most real-time scheduling problems are known to be NP-hard, an approximation approach that applies heuristic methods using conventional computer algorithms has been used to solve these scheduling problems. Artificial Intelligence (AI) planners have been used extensively in manufacturing scheduling and operations research. In this paper, we demonstrate the idea of using AI planners to perform scheduling through an example. We derive a solution to scheduling several image tasks on a distributed computer system, using the AI planner PRODIGY. The basic characteristics of AI planners in general and the PRODIGY solver in particular are described, the domain theory and problem specification for our problem through the PRODIGY description language PDL are presented.
Deoxyribonucleic acid ploidy was determined in paraffin-embedded tumor tissue from 116 patients with primary oral squamous cell carcinomas (including 5 carcinomas of the lip and 14 of the tongue) by means of flow cyto...
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Deoxyribonucleic acid ploidy was determined in paraffin-embedded tumor tissue from 116 patients with primary oral squamous cell carcinomas (including 5 carcinomas of the lip and 14 of the tongue) by means of flow cytometry. One hundred six cases were suitable for evaluation (91%). Sixty-eight percent of the cases (n = 72) showed a nondiploid nuclear DNA content. Nondiploidy correlated significantly with presence of lymph node metastases (p < 0.02) but not with tumor stage, grading (World Health Organization), or relapse-free and overall survival. Carcinomas of the lip and tongue turned out to be diploid more frequently than other oral squamous cell carcinomas (p = 0.002). In the 21 cases in which a comparison of DNA content of excisional biopsy specimens and subsequent resection specimens was possible a difference in DNA ploidy was found in one case only. The comparison of primary tumors and their lymph node metastases in 30 cases revealed a discrepancy of DNA content in five cases (17%), which was connected with a shift from nondiploidy to diploidy in four out of five cases. Fifty cases studied in parallel by means of image cytometry with Feulgen-stained tissue sections exhibited a concordance of the ploidy status in 87% and a significant correlation of the DNA index values obtained with both methods (p < 0.01). These results demonstrate that DNA ploidy in oral squamous cell carcinomas is distributed rather homogeneously within the tumors and remains rather stable in the lymph node metastases. Despite a significant correlation between nondiploidy and presence of lymph node metastases, ploidy failed to be a statistically significant parameter for prognosis in oral squamous cell carcinomas in our investigation.
High level data parallel languages such as Vienna Fortran and High Performance Fortran (HPF) have been introduced to allow the programming of massively paralleldistributed memory machines at a relatively high level o...
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High level data parallel languages such as Vienna Fortran and High Performance Fortran (HPF) have been introduced to allow the programming of massively paralleldistributed memory machines at a relatively high level of abstraction, based on the single program multiple data (SPMD) paradigm. Their main features include mechanisms for expressing the distribution of data across the processors of a machine. The paper introduces additional language functionality to allow the efficient processing of sparse matrix codes. It introduces methods for the representation and distribution of sparse matrices, which forms a powerful mechanism for storing and manipulating sparse matrices able to be efficiently implemented on massively parallel machines.< >
This paper presents a parallel approach to a hierarchical image matching scheme using the Hausdorff distance for object recognition and localization in aerial images. Unlike the conventional matching methods in which ...
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This paper presents a parallel approach to a hierarchical image matching scheme using the Hausdorff distance for object recognition and localization in aerial images. Unlike the conventional matching methods in which edge pixels are considered as image feature pixels, the distance transform and the blind pointwise comparison procedure is simplified and extended in terms of the Hausdorff distance, and a guided image matching system is developed by the hierarchical detection of interesting points via a dynamic thresholding scheme for the search of the best matching between two image sets. Furthermore, the concept of remote procedure call (RPC) in distributed systems is introduced for the parallel implementation to achieve the speedup without specific software and hardware requirements.
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