背景自适应的GrabCut图像分割算法

Adaptive Background Image Segmentation Algorithm Based on GrabCut

图割用于图像分割需用户交互,基于激光雷达传感器,提出了阈值法得到目标的外截矩形,再映射到图像完成交互.针对GrabCut算法耗时、对局部噪声敏感和在复杂背景提取边缘不理想等缺点,提出了背景自适应的GrabCut算法,即在确定背景像素中选取可能目标像素邻近的一部分像素作为背景像素,使背景变得简单,尤其适用于前景像素在整幅图中所占比例较小和在目标像素周围的背景相对简单的情况.实验结果表明,所提算法与GrabCut算法相比,减少了图的节点数,降低了错误率,有效的提高了运行效率,提取的目标边缘信息更加完整、平滑.

Graph cut needs user interaction on image segmentation, which comes up with that threshold value method, gets the objects by cutting rectangle and then we remaps to image to finish the interaction by laser radar. GrabCut algorithm is sensitive to local noise, and it is time-consuming. In addition, the edge extraction is not ideal under complex background, so an improved GrabCut algorithm is put forward to adapt background automatically in the determined background. The proposed algorithm chooses probable foreground neighboring pixels as background pixels to make background become simple. It is applicable to the case when foreground pixels account for low proportion in the whole image pixels and the background pixels are relatively simple around the foreground. Experimental results show that error rate of the proposed algorithm is reduced and the efficiency is improved in comparison with GrabCut algorithm after reducing nodes number in the graph. In addition, the edge extraction is more complete and smooth.