Monocular depth cue fusion for image segmentation and grouping in outdoor navigation

This paper proposes an efficient fusion strategy of monocular depth cue and other image features for natural image segmentation and grouping. The main idea is to improve the performance of image clustering via fusing depth cue, color, spatial location, and edge confidence in six-dimensional color-depth feature space. It integrates the monocular depth cue estimation, mean shift filtering and graph cuts algorithm together. Firstly, the dark channel prior based atmospheric transmission estimation is employed to recover monocular depth cue. Then the mean shift filtering in the weighted color-depth space is proposed to obtain cluster regions with correct boundaries. Finally, graph cuts algorithm is applied to achieve the final regional grouping. Experimental results indicate the proposed method has excellent performance in outdoor natural environments.