Object Detection for Robotic Applications Using Perceptual Organization in 3D

Abstract

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is still a challenging task in computer vision. A framework is introduced to segment RGB-D images where data is processed in a hierarchical fashion. After pre-segmentation and parametrization of surface patches, support vector machines are used to learn the importance of relations between these patches. The relations are derived from perceptual grouping principles. The proposed framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. Furthermore, the problem of segmenting partially occluded objects is tackled.