COMPUTER VISION BASED ON A HYPOTHESIZATION AND VERIFICATION SCHEME BY PARALLEL RELAXATION

Shape recovery from a monocular image is addressed. It is often said that the information conveyed by an image is insufficient to reconstruct 3D shapes of objects in the image. This implies that shape recovery from an image necessitates the use of additional plausible constraints on typical structures and features of the objects in an ordinary scene. We propose a hypothesization and verification method for 3D shape recovery based on geometrical constraints peculiar to man-made objects. The objective is to increase the robustness of computer vision systems. One difficulty with this method lies in the mutual dependency between proper assignment of constraints to the regions in a given image and recovery of a consistent 3D shape. A concurrent mechanism has been implemented which is based on energy minimization using a parallel network for relaxation. This mechanism is capable of maintaining consistency between constraint assignment and shape recovery.