Image Segmentation by Fusing Color and Depth Information for Region Merging
Chao Jia, Lei Guan, Yong Huang · 2023
When there are occlusions and shadows in the image, it is difficult for the traditional algorithm to extract the object contour accurately by using only the appearance information. In this paper, an improved Mean-shift algorithm is used to obtain the initial set of oversegmentation subregions. Then the color contrast of the subregions is calculated, the depth contrast is calculated in the depth image, and the fusion contrast is obtained by a dynamic weighted curve fitting method. Finally, the seed regions of the target and background are automatically selected according to the depth information, and MSRM algorithm is used to complete the image segmentation based on the principle of maximum similarity. Experiments on NYU-V2 and Middlebury database show that compared with the current general algorithm, the proposed algorithm can improve the accuracy of segmentation and improve the visual effect of segmentation images.