摘要
对颜色空间做了全面地介绍,对当前传统的彩色图像分割技术进行了较为系统的论述,通过比较发现传统的单一分割算法在彩色图像分割中都不可避免地存在一定的不足与缺陷。而以神经网络、遗传算法和蚁群算法等智能算法进行图像分割时,由于从不同侧面反映了人类视觉感知的智能性、并行性,取得了较好的效果,推动了图像分割向智能化方向发展,但其在理论和实践上都没有达到让人满意的程度。因此,可以根据实际情况组合不同的算法,分层次地分割图像,寻找符合人类视觉感知特性的有效的彩色图像分割混合智能算法,从而弥补单一算法对彩色图像分割的不足。
Common color space is introduced, and the current traditional color image segmentation techniques are introuduced. Comparing with their respective advantages and disadvantages, it is found that the traditional single arithmetic ofcolor image segmentation inevitably has some deficiencies and defects. Image segmentation combined with artificial neural network or genetic algorithm is a new research field with rapid development in recent years. The methods have better effect, in that they have human-like intelligence and concurrency, Although many works have been done in this area, we still have a long way to get a satisfactory result. So, we can combine different intelligent algorithms according to the actual situation or have a hierarchical division of image segmentation. By means of these, more effective segmentation algorithms are found in accord with human visual and perceptive traits, and it can make up for existing deficiencies and shortcomings.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第16期4260-4262,F0003,共4页
Computer Engineering and Design
关键词
彩色图像分割
混合智能算法
颜色空间
神经网络
遗传算法
蚁群算法
color image segmentation
hybrid intelligent computing
color space
neural networks
genetic algorithm
ant colony algorithm
作者简介
张学习（1978-），男，安徽萧县人，博士研究生，讲师，研究方向为智能控制和信息处理技术,E-mail：zxxnet_2@163．com
杨宜民（1945-），男，广东潮汕人，教授，博士生导师，研究方向为智能控制和人工智能。
