<title>New 3D reconstruction approach for 3D object recognition in neuro-vision system</title>

Abstract

In this paper, a new 3D reconstruction approach for 3D object recognition in neuro-vision system is presented. First, a phase based stereo matching using Hopfield neural network approach is presented, the stereo matching problems are treated in frequency domain by using local phase. Instead of matching feature or texture of images, the stereo matching process is performed by using local phases of left image and right image in stereo image pair. The Hopfield neural network is adopted to implement the stereo matching process. A suitable architecture of neural network is established, so that the computation can be implemented efficiently in parallel. A suitable matching function is created by using the local phase property. The energy function for neural network is constructed with satisfying some necessary constraints. The stereo matching process them is carried to find the minimum energy corresponding to the solution of the problem. Second, a 3D object reconstruction neural networks is constructed by using BP neural network. So the 3D configuration and shape can be reconstructed by this neural network. With multiple neural networks the 3D reconstruction processes can be performed in parallel. The examples for both synthetic and real images are shown in the experiment, and good results are obtained.