基于一种新的目标识别的边缘爬行算法

针对机器人目标识别的实时性、快速性和鲁棒性特点,依据物体边缘大部分区域的方向缓变原理,提出了一种基于记忆的边缘爬行改进算法.爬行搜索方法的一个明显缺点是"爬虫"容易掉进"陷阱",即围绕某个局部小区域重复爬行.记忆搜索方法则有效克服了爬行搜索方法容易掉进"局部陷阱"的不足.在边缘爬行的同时,通过标号将颜色相近的不同物体分割出来.实验结果表明,与传统方法比较,该方法的边缘线更加完整、清晰,而且缩短了处理图像的时间,使目标识别的实时性和鲁棒性得到了优化.

Object Recognition Based on a New Method of Edge Crawling

According to the characteristics of real-time, rapidness, robustness in object recognition and the slow changrog of the most edge directions, a new memory-based edge crawling method was proposed in this paper. An obvious flaw of the edge crawling was that the bug was easy to be trapped and kept crawling around a certain local region. Memory-based searching algorithm could remedy the shortcoming of the edge crawling effectively. This method segmented different objects having the similar colours by marking the extracting edge during the course of edge crawling. The experimental results demonstrated that, compared with traditional approaches, the adopted approach was able to get more complete and clear contour, shorten the time of image processing and therefore optimize the accuracy and robustness of object recognition.