As part of the presentation, we will describe a technique for merging the inverse wavelet compression and correlation filtering operations into a seamless process. In addition to being theoretically elegant, this has the added benefit of reducing the overall computations. Equally significant, it offers a method for obtaining the correlation result without requiring the full image to be reconstructed thus avoiding the need for large amounts of storage. We also show that it is indeed possible to design the compression filters and the correlation filter in a joint optimization process with added benefits. The notion of performing recognition by directly exploiting the Wavelet coefficients is also addressed. Here, we describe a technique which combines the information in different bands using a multi-channel correlation algorithm known as polynomial correlation filters. The optimization process must take into account the shift-sensitivity of wavelet coefficients. It is shown that simultaneous optimization of the sub-band QMFs and the correlation filters leads to promising results.
ACCESS THE FULL ARTICLE
Abhijit Mahalanobis, Cindy Daniell, "Correlation pattern recognition in compressed images," Proc. SPIE 10302, Optoelectronic Information Processing: Optics for Information Systems: A Critical Review, 1030208 (30 November 2001);