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Intelligent image processing based on distributed-cooperative environment

基于分布式协作环境的智能图像处理

基本信息

批准号：
14580435
负责人：
WATANABE Eiji
金额：
$ 2.37万
依托单位：
Konan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

项目摘要

(1)Intelligent learning of neural networks for image processing :(a)An image segmentation method using multiple multi-layered neural networks has been newly introduced and an image segmentation method is proposed based on the differences among the compression abilities in multiple NNs. The segmentation results by the proposed method have been concretely shown by some experiments.(b)A cooperative learning algorithm for multi-layered neural networks is proposed.The proposed algorithm has the following procedures ; (i)parallel learning of each NN with different initial weights by using the back propagation learning algorithm, (ii)exchange of weights so as to minimize the unbalance among the group of NNs and improve the learning performance of them. We have applied the proposed algorithm for a classification problem of Japanese-Kanji fonts and have shown its behavior and effectiveness.(2)Image Restoration :(a)Considering that a partial image which contains some edges tends to become a bina … More ry image, we introduced a new evaluation function that takes a maximum for binary images and proposed an automatic extracting method of an edge area from a degraded image using the function.(b)We discussed the problem to estimate an original image and a point spread function from only a degraded image. We showed that there exist finite solutions if a degraded image has a compact support. Then, we proposed an algorithm to find all solutions.(3)Considerations on the structure of a distributed and cooperative computational environment :Our distributed and cooperative computational environment is based on a PC cluster, Unix-like operating systems, and MPI. We proposed a method of the dynamic load balancing on such environment. In the method, we regarded a cluster and a PC cluster system as an autonomous agent and a multi-agent system respectively. The cluster is designed using the meta-level architecture which consists of base level for usual computations and meta level for global computations. According to the method, the dynamic load balancing on such environment is performed as the communication and load balancing on the multi-agent system. Less

(1)神经网络在图像处理中的智能学习：(a)引入了一种基于多层神经网络的图像分割方法，提出了一种基于多层神经网络压缩能力差异的图像分割方法。实验结果表明了该方法的分割效果。(b)提出了一种多层神经网络的协同学习算法。本文提出的算法有以下步骤：(i)使用反向传播学习算法对具有不同初始权值的每个NN进行并行学习；（ii）交换权值，以最小化NN组之间的不平衡，提高NN组的学习性能。我们将该算法应用于一个日文汉字字体分类问题，并展示了它的行为和有效性。(2)图像恢复：(a)考虑到含有部分边缘的局部图像容易成为二值化图像，引入了对二值化图像取最大值的评价函数，并提出了一种利用该函数从退化图像中自动提取边缘区域的方法。(b)讨论了仅从退化图像中估计原始图像和点扩展函数的问题。我们证明了退化图像具有紧支持时存在有限解。然后，我们提出了一种求所有解的算法。(3)分布式协作计算环境的结构考虑：我们的分布式协作计算环境基于PC集群、类unix操作系统和MPI。提出了一种基于此环境的动态负载均衡方法。在该方法中，我们将集群和PC集群系统分别视为自治代理和多代理系统。集群采用元级架构进行设计，该架构由用于常规计算的基级和用于全局计算的元级组成。根据该方法，将这种环境下的动态负载均衡作为多智能体系统上的通信和负载均衡。少