Quantifying saliency of feature points on 3-D curved surfaces from range images

A method for quantifying saliency of 3D feature points from range images of model surfaces is proposed. The saliency is characterized by the discriminating power of the feature points for distinguishing the model objects they belong to from others. It is measured by local shape similarity, and the similarity coefficients of surface points are calculated by parameterizing local surface representations into the local orientation coordinate systems. An algorithm for performing the Hough transform weighted by the similarity coefficients is developed to optimally determine the saliency coefficients of the feature points. It is shown that the method is especially useful for dealing with the occlusion problem that must be solved when constructing a flexible robot vision system