A Transformation-Invariant Relaxation Scheme for Feature Mapping

Author: Sei-Wang Chen, Chien-Yun Dai(Department of Computer and Information Education, National Taiwan Normal University)

Abstract：

A large number of relaxation schemes for feature mapping, claimed to be invariant to transformation, have been reported. However, most of them can deal with transfor-mations involving only rotation and translation, but not scaling. To stay away from the issue of scaling, unrealistic assumptions have to be imposed, such as the conjectures that range data are available so that objects can be rescaled before mapping, and that object shapes are complete so that ratios between object shapes and prototypes can be figured out beforehand. In this paper, we propose a relaxation scheme which is able to be in-variant at a time to rotation, translation, as well as scaling. In addition, the proposed scheme can also cope with shapes that may be distorted and incomplete. Our scheme has been tested on both synthetic and real data. Experimental results manifest that the proposed scheme is applicable.

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