Severe-occluded 3D object identification via region-based descriptions

Abstract

This paper describes a region-based strategy for part-based object identification with independence of the external factors that affect its captured image: light variations, capture point-of-view or occlusions. Starting from color images and depth estimations, i.e. not requiring 3-dimensional models, we focus on the identification of learned objects in severe-occlusion scenarios. To face this problem, we assume that objects have been preliminarily segregated from the scene. Strong changes of appearance—due to one or several of the aforementioned factors or to the object nature, e.g. deformable objects—substantially increase the problem complexity. The proposed algorithm operates by splitting segregated objects in successively coarser region-partitions, with each region representing a part of the object from which it was extracted. For the characterization of these parts, two region-driven descriptors are proposed: R-DAISY and R-SHOT. Their novelty relies on the use of a size-and-shape-variable description support which is automatically defined by the object part itself. Descriptions obtained in this way are self-organized in a single neural structure by an unsupervised learning process. Experimental results are promising in the identification of severe-occluded objects using a small set of training instances—1-to-8 short-varied views per object.