A neural network approach for 3-D object recognition

In this paper we propose a new algorithm for recognizing 3-D objects from 2-D image. The algorithm takes the multiple view approach in which each 3-D object is modeled by a collection of 2-D projections from various viewing angles where each 2-D projection is called an object model. To select the candidates for the object model that has the best match with the input image, the proposed algorithm computes the surface matching score between the input image and each object model by using Hopfield nets. In addition, the algorithm gives the final matching error between the input image and each candidate model by the error of the pose-transform matrix proposed by Hong et al.[1989] and selects an object model with the smallest matching error as the best matched model