Resolution selection using generalized entropies of multiresolution histograms

Abstract

The performances of many image analysis tasks depend on the image resolution at which they are applied. Traditionally, resolution selection methods rely on spatial derivatives ofimage intensities. Differential measurements, however, are sensitive to noise and are local. They cannot characterize patterns, such as textures, which are defined over extensive image regions. In this work, we present a novel tool for resolution selection that considers sufficiently large image regions and is robust to noise. It is based on the generalized entropies ofthe histograms ofan image at multiple resolutions. We first examine, in general, the variation ofhistogram entropies with image resolution. Then, we examine the sensitivity ofthis variation for shapes and textures in an image. Finally, we discuss the significance ofresolutions ofmaxim um histogram entropy. It is shown that computing features at these resolutions increases the discriminability between images. It is also shown that maximum histogram entropy values can be used to improve optical flow estimates for block based algorithms in image sequences with a changing zoom factor.