Evolutionary Computation Schemes based on Max Plus Algebra and Their Application to Image Processing

H. Nobuhara, Chang-Wook Han
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引用次数: 2

Abstract

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is proposed. Through the image compression/reconstruction experiment using test images extracted from standard image database (SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed learning method is better than that obtained by the conventional method.
基于Max +代数的进化计算方法及其在图像处理中的应用
提出了一种基于混合遗传算法的形态神经网络学习方法。形态学神经网络是基于max-plus代数的,因此很难通过导数运算的学习方法来优化MNN的系数。为了解决这一难题,提出了一种基于混合遗传算法的MNN系数优化学习方法。通过从标准图像数据库(SIDBA)中提取的测试图像进行图像压缩/重建实验,证实了所提出的学习方法获得的重建图像质量优于传统方法获得的图像质量。
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