Pyramid-Based Extraction Of Local Image Features With Applications To Motion And Texture Analysis

Multi-resolution pyramid structures may be used to compute image properties efficiently and within sets of Gaussian-like windows of many sizes. We define several basic pyramid algorithms which may be applied to a variety of image understanding tasks. These include a multi-resolution low-pass filter (the Gaussian pyramid), a multi-resolution band-pass filter (the Laplacian pyramid) and a multiscale window function (the Hierarchical Discrete Correl-ation). To illustrate these algorithms we present procedures for computing local edge density and spectral energy estimates for texture discrimination, and procedures for computing local correlation and gradient based estimates of pattern displacement for motion analysis. Pyramid computations are shown to be particularly low in both cost and complexity.