摘要
针对基于二维直方图的分割方法存在计算耗时的缺点,将群体智能中的粒子群优化算法应用到图像分割中,提出了一种新的边缘检测算法。新方法在定义的二维灰度空间中,利用粒子群优化算法自适应搜索最优解,并以此作为边缘检测算子的门限,阈值变换后便可得到表示原图像主要特征的分割结果。通过对水下图像处理的实验证明,该算法对简单背景的图像分割是有效的,和传统检测方法相比,具有更好的抗噪性能。
Due to the assimilation of the water and uneven lightness, the underwater image would have low S/N and the edge is fuzzy. If the traditional methods based on one-dimensional histogram are used to dispose it directly, the result is not expected. The two-dimensional maximum entropy method not only considers the distribution of the gray information, but also takes advantage of the spatial neighbor information derived from the two-dimensional histogram of the images. As a global threshold method, it often gets ideal segmentation results. However, its time-consuming computation is often an obstacle in real time application systems. In this paper, the edge detection approach based on the particle swarm optimization algorithm and the two-dimensional grayscale histogram is proposed to deal with underwater image. In the proposed method, the particle swarm optimization algorithm is realized successfully in the process of searching the optimal solution （s, t） in the twodimensional grayscale space. Then the optimal solution （s, t）, where s is a threshold for pixel intensity and t is another threshold for the local average intensity of pixels, is selected as the threshold of edge detection operators. The experiments of segmenting the underwater images are illustrated to show that the proposed method is effective. Comparing with the traditional methods, this approach shows better adaptive and noise restraining performance.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2007年第7期1192-1196,共5页
Systems Engineering and Electronics
关键词
边缘检测
粒子群优化
二维直方图
水下图像
edge detection
particle swarm optimization
two-dimensional maximum entropy
underwater image
