融合深度和颜色信息的图像物体分割算法

讨论立体图对的图像分割问题，提出一种基于深度和颜色信息的图像物体分割算法。该算法首先利用基于聚类的Mean-shift分割算法对目标图像进行适度的过分割，同时借助双目立体视觉算法获取立体图对的稠密深度图，并依据深度不连续性从过分割结果中选取用于继续进行“精致”分割的种子点集，接着对未分配种子标签的区域用图割算法分配标签，并对彼此之间没有深度不连续边界但具有不同标签的相邻区域进行融合。相比于传统图像分割算法，该算法可有效克服过分割和欠分割问题，获取具有一定语义的图像分割结果。相关的对比实验结果验证了该算法的有效性。

Image Object Segmentation Algorithm by Combining Depth Discontinuities and Color Information

The image segmentation using stereo image pairs is discussed. An image segmentation algorithm combining depth discontinuities and color information is proposed. Mean-shift segmentation algorithm is applied to the over-segmentation of the image,and meanwhile the dense depth map of the image pairs can be calculated by using stereo vision algorithm. Then,through combining color image over-segmentation and depth discontinuities,multiple seed regions for accurate segmentation are selected along the depth discontinuities. By using graph cut algorithm,unlabeled regions are assigned with seed regions′ labels. Next,the neighbor regions with different labels but without discontinuous depth boundary between them are merged together as well. Compared with the traditional feature clustering image segmentation algorithms,the proposed algorithm overcomes the problems of over-segmentation and under-segmentation,and semantic object segmentation results can be achieved. Experimental results show the validity of the proposed algorithm.