Multiresolution Moment Filters: Theory and Applications

We introduce local weighted geometric moments that are computed from an image within a sliding window at multiple scales. When the window function satisfies a two-scale relation, we prove that lower order moments can be computed efficiently at dyadic scales by using a multiresolution wavelet-like algorithm. We show that B-splines are well-suited window functions because, in addition to being refinable, they are positive, symmetric, separable, and very nearly isotropic (Gaussian shape). We present three applications of these multiscale local moments. The first is a feature-extraction method for detecting and characterizing elongated structures in images. The second is a noise-reduction method which can be viewed as a multiscale extension of Savitzky-Golay filtering. The third is a multiscale optical-flow algorithm that uses a local affine model for the motion field, extending the Lucas-Kanade optical-flow method. The results obtained in all cases are promising.

Published in:
IEEE Transactions on Image Processing, 13, 4, 484–495

 Record created 2005-11-30, last modified 2018-01-27

External links:
Download fulltextURL
Download fulltextURL
Download fulltextn/a
Rate this document:

Rate this document:
(Not yet reviewed)