Depth-aware layered edge for object proposal
Object proposal, typically served as preprocessing of various multimedia applications, aims to detect the bounding boxes of possible objects in an image. In this paper, we propose a novel object proposal method for RGB-D images based on layered edges, which can effectively eliminate the influence of the mixture of edges from objects and background and improve the accuracy of proposals. Firstly, we detect the sparse edges and correct depth on super-pixel representation. Then, we use depth-adaptive sliding windows in sampling of depth distribution and measure the objectness of each candidate box in multiple depth layers. Finally, the candidate boxes are ranked according to the integrated scores of all the depth layers, and the final proposals are generated. The experimental results show that the proposed method can outperform the state-of-the-art methods on the largest RGB-D image dataset for object proposal.