A scale space based deformable template matching algorithm

This paper presents a scale space based deformable template matching algorithm. The deformable templates are greylevel patterns with clearly defined image features to represent ideal segmentation results of some generic percepts. To segment a specific target in an image, the algorithm deforms the corresponding generic template to match the actual state of the target To reduce the probability of being stuck in local minima and to speed up the process of convergence, the algorithm deforms the templates in scale space from coarse to fine and uses the normalized cross-correlation to provide initial stares for the deformation process. To achieve the best accuracy for locating object boundaries, the algorithm also employs the 2D optimal edge detection functional developed by Qian and Huang at the finest scale. Experimental results on real images are given in the paper.