基于子区生长的移动机器人视觉图像分割

Visual Image Segmentation of Mobile Robot Based on Sub-Region Growing

针对区域生长法中自播种过程消耗了大部分时间的问题,提出了基于子区生长的图像分割方法,以提高图像分割效率.该方法在具有区域一致性的子区生长基础上,将分割与自播种过程结合在一起,提高了图像分割速度.同时针对图像噪声干扰子区一致性检测问题,提出采用区域像素平均距离作为区域一致性标准.实验结果表明:区域像素平均距离比传统的方差具有更好的噪声抑制能力;移动机器人视觉图像分割实验验证了所提出的基于子区生长的图像分割法的有效性.

The automatic seeded process expends much time in the region growing method, Focused on this problem, an image segmentation method based on sub-region growing is proposed, to advance the efficiency of image segmentation. Segmentation and automatic seeded process are combined based on the sub-regions which grow coherently, and the segmentation speed is increased. Aimed at that, image noise affects detection of region coherence, the mean distance of region pixels used as the standard of region coherence is presented at the same time. Experiments showed that the mean distance of region pixels is better than traditional squares of subtraction on the capability of noise suppressed; Experiments of visual image segmentation of mobile robot showed the presented method to be effective.