Segmentation of depth images into objects based on local and global convexity

Abstract

An approach for object detection in depth images based on local and global convexity is presented. The approach consists of three steps: image segmentation into planar patches, greedy planar patch aggregation based on local convexity and segment grouping based on global convexity. The proposed approach improves upon existing similar methods, which use convexity as a cue for object detection, by detecting convex objects represented by multiple spatially separated image regions as well as hollow convex objects. The presented method is experimentally evaluated using a publicly available benchmark dataset and compared to two state-of-the art approaches. The experimental analysis demonstrates improvement achieved by high-level segment grouping based on global convexity.