A Knowledge-based Strategy for Object Recognition and Reconstruction

Object recognition and reconstruction are pivot technologies both in computer vision and in machine intelligence. There are a number of techniques for object recognition, such as pattern comparison, feature matching, and boundary detection and so on, which heavily depend on the rigid depiction of projected images of the object. This consequently results in that even recognized it is still remain tough to reconstruct the target object. In this paper, we propose a novel method for object recognition and reconstruction based on shape knowledge, which is constructed as a repository of three-dimensional shapes to reinforce the system intelligence. The method employs a feature matching algorithm to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized. Preliminary experiment results show that the proposed method can effectively compensate the inadequacy of information in shape reconstruction and deliver the satisfied three-dimensional information for the recognized object. This is rather helpful in image based object recognition and reconstruction where the three-dimensional topological information of the object is almost empty.