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

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.