Hierarchical image matching: a chamfer matching algorithm using interesting points

Image matching in conjunction with a distance transform has played an important role in computer vision and image analysis. This paper presents a new hierarchical chamfer matching algorithm based on the detection of interesting points. The algorithm extends the traditional method by introducing interesting points to replace edge points in the distance transform for the matching measurement. A series of images, with different numbers of interesting points featuring in the original image, is created in a pyramid structure through a dynamic thresholding scheme. The matching is performed in this pyramid in a coarse-to-fine level order, by minimizing a given matching criterion in terms of the distance between selected interesting points. This hierarchical structure aims to reduce the computational load. The algorithm is simple to implement and quite insensitive to noise and other disturbances. In addition, such a hierarchical matching scheme is implemented on a low-cost heterogeneous PVM (Parallel Virtual Machine) network to speed up the processing without any specific software and hardware requirements