A non-linear corresponder for 2D automated point distribution model construction

This paper presents a non-linear automated point distribution model building method (NAMB). In this algorithm, each training sample is registered to all other training samples by a non-linear corresponder. When landmarks are chosen by regarding all shapes of samples, a point distribution model (PDM) is constructed. The contribution of this paper is an accurate non-linear points correspondence detection algorithm and a framework that preserves shape details of training samples. In the experiment, the NAMB is compared with the manual model construction and other automated method to construct PDMs. The comparison illustrated that the NAMB is faster than manual method. At the same time, NAMB is more capable than the other automated method in preserving shape details.