STEERABLE FILTERS GENERATED WITH THE HYPERCOMPLEX DUAL-TREE WAVELET TRANSFORM

The use of wavelets in the image processing domain is still in its infancy, and largely associated with image compression. With the advent of the dual-tree hypercomplex wavelet transform (D- HWT) and its improved shift invariance and directional selec- tivity, applications in other areas of image processing are more conceivable. This paper discusses the problems and solutions in developing the DHWT and its inverse. It also offers a practical implementation of the algorithms involved. The aim of this work is to apply the DHWT in machine vision. Tentative work on a possible new way of feature extraction is presented. The paper shows that 2-D hypercomplex basis wave- lets can be used to generate steerable filters which allow rotation as well as translation.