A novel algorithm for efficient depth segmentation using low resolution (Kinect) images

Object segmentation is a fundamental research topic in computer vision. While, only the color information for object segmentation has been the main focus of research, with the availability of low cost color plus range sensors, depth segmentation is now attracting significant attention. This paper presents a novel algorithm for depth segmentation. The proposed technique exploits the divergence of the 2D vector field to segment three-dimensional (3D) object in the depth maps. For a given depth image acquired using a low resolution Kinect sensor, a 2D vector field is computed first at each point of the range image. The depth map is then converted to the div map by computing the 2D vector field's divergence. The latter maps the vector field to a scalar field. The variation of divergence values over the surface contour of the 3D object helps to extract its boundaries. Finally, the depth segmentation is accomplished by applying a threshold to the div map to segment 3D object from the background. In addition to removing the background, the proposed technique also segments the object from the surface on which the object is positioned. The proposed technique was tested on low resolution Washington RGB-D (Kinect) object dataset. Preliminary experimental results suggest that the proposed algorithm achieves better depth segmentation compared to state-of-the art graph-based depth segmentation. The proposed technique also outperforms the latter by achieving 40% higher computational efficiency.