ABSTRACT
Shape feature plays a critical role in many graphics and visualization tasks. There are many strategies to define features. Among them, wavelet transform is widely used, because it extracts detailed information of a function at different scales. In image processing, the well known scale-invariant feature transform (SIFT) [Lowe 04] is a wavelet-based method. SIFT boosted many high-level problems in computer vision with robust features. One reason behind the scene is that human vision is very sensitive to second-order derivatives. The difference of Gaussians, as a wavelet, is believed to mimic how neural processing in the retina of the eye extracts details from images destined for transmission to the brain [Young 87].
