Automated landmark points detection by using a mixture of approaches

This paper deals with the automated detection of a closed curvećs dominant points. We treat a curve as a 1-D function of the arc length. The problem of detecting dominant points is translated into seeking the extrema of the corresponding 1-D function. Three approaches for automated dominant points detection are presented: (1) an approach based on fitting polynomial, (2) an approach using 1-D computer registration and (3) an innovative approach based on a multi-resolution scheme, zero-crossing and hierarchical clustering. Afterwards, two methods are introduced based on the linearly and non-linearly mixing the results from the three approaches. We then mix the results in a mean-square error sense by using the linear and non-linear fittings, respectively. We experimentally demonstrate the problem of detecting 21 landmarks on 38 vole-teeth that by mixing, the detection accuracy is improved by up to 41.47 % with respect to the results for individual approaches, as applied within the mixture