Geometric/Photometric Consensus and Regular Shape Quasi-Invariants for Object Localization and Boundary Extraction
Polyhedral descriptions of objects are needed in applications like vision-based robotics, e.g. to carry out grasping and assembling tasks. This work presents a novel methodology for the subtask of localizing a three-dimensional target object in the image and extracting the two-dimensional depiction of the boundary. By eliciting the general principles underlying the process of image formation we exhaustively make use of general, qualitative assumptions, and thus reduce the role of object-specific knowledge for boundary extraction. Geometric/photometric consensus principles are involved in a Hough transformation based approach for extracting line segments. The perceptual organization of line segments into polygons or arrangements of polygons, which originate from the silhouette or the shape of approximate polyhedral objects, is based on shape regularities and quasi-invariants of projective transformation. An affiliated saliency measure combines evaluations of geometric/photometric consensus features with geometric grouping features. An ordered set of most salient polygons or arrangements is the basis for locally applying techniques of object recognition or detailled boundary extraction. The generic approaches are demonstrated for technical objects of electrical srap located in real-world cluttered scenes.