摘要
针对二值图像角检测提出了一种改进的形态学检测算法,此算法利用了圆盘算子的对称性,避免了结构元的旋转,提高了检测算法的运行效率.然后提出了一种改进的EM图像分割方法对灰度及彩色图像进行分割处理,增强了分割后图像的区域特征,为形态学灰度及彩色图像的角检测提供了更为合理的检测环境.实验结果证明在含有噪声及复杂背景情况下,该方法仍可获得较好的检测结果.
Introducing the symmetry of circular operator, an improved morphological detection algorithm is proposed for binary images to avoid the rotation of structural elements so as to raise the detection efficiency. Then, an improved method for EM image segmentation is proposed for gray and color images to enhance the regional features after segmentation, thus providing more reasonable conditions morphologically for the corner detection of gray and color images. Experimental results verified that the method can get better detection result even if the comer detection is done in a noisy or complex background.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第11期1536-1539,共4页
Journal of Northeastern University(Natural Science)
基金
公安部重点项目(20029322301)
黑龙江省自然科学基金资助项目(F0318)
关键词
数学形态学
角检测
角估计
自学习
图像分割
mathematical morphology
corner detection
angle estimation
self-learning
image segmentation
