Range surface characterization and segmentation using neural networks

Abstract

This paper presents an integrated neural net-based approach to the segmentation of range images into distinct surfaces, which is an essential step in range image analysis and interpretation. A two-stage connectionist neural net model is proposed which extracts local surface features at each image point and groups pixels via local interactions among different features. The first stage computes surface parameters, e.g., surface normals, curvature and discontinuities (crease and jump) by optimally projecting the local range profile onto a set of non-orthogonal basis functions. In the second stage, adjacent pixels compete with each other based on the surface features associated with them to group themselves into different surface patches. Daugman's projection neural net (DPNN) and Kohonen's self-organizing neural net (KSNN) are used for the feature extraction and region-growing, respectively. Empirical performance analysis shows that the feature extraction using neural net is quite robust with respect to the additive noise. Experimental results are included to demonstrate the performance of the proposed technique.