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）用于图像处理的神经元网络的智能学习：（a）新引入了使用多个多层神经元网络的图像分割方法，并根据多个NN中的压缩能力之间的差异提出了图像分割方法。提出了一些实验的分割结果。（b）提出了用于多层神经元网络的合作学习算法。该算法具有以下过程；（i）通过使用背部传播学习算法，（ii）权重交换以最大程度地减少NNS之间的不平衡并提高其学习成绩的不平衡。 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 tend 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仅从退化的图像中估算原始图像和点扩散函数的问题。我们表明，如果降解的图像具有紧凑的支持，则存在有限的解决方案。然后，我们提出了一种算法来查找所有解决方案。（3）有关分布式和协调的计算环境结构的考虑：我们的分布式和协调的计算环境基于PC群集，类似UNIX的操作系统和MPI。我们提出了一种在这种环境下的动态负载平衡的方法。在该方法中，我们将群集和PC群集系统分别视为自主代理和多代理系统。该群集是使用元级体系结构设计的，该体系结构由通常的计算基础级别和全球计算的元级别组成。根据该方法，在此环境上的动态负载平衡是在多代理系统上的通信和负载平衡。较少的