Shape recovery and error correction based on hypothetical constraints by parallel network for energy minimization
Shape recovery from a monocular image with errors is addressed. Shape recovery necessitates the use of additional plausible constraints on typical structures and features of the objects in an ordinary scene. We propose an hypothesization and verification method for 3D shape recovery based on geometrical constraints peculiar to man-made objects. 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. Another lies in dealing with the error by preliminary processes to extract 2D geometrical features from a real image. A concurrent mechanism has been implemented which is based on energy minimization using a parallel network for relaxation, and is capable of maintaining consistency between constraint assignment and shape recovery. The error in an input image is also corrected through the process of shape recovery.
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