A 2D multirate Bayesian framework for multiscale feature detection

Abstract

This paper presents a novel methodology for designing a 2D multiscale feature detector, which consists of a filter bank and a maximum a posteriori (MAP) classifier. The framework assumes the availability of a one-scale filter with a particular indicator response to the desired feature. This filter is used to generate a multiscale set of discrete filters by sampling on a rectangular lattice to preserve the indicator responses at all the scales. The net step in the framework consists of designing the filter bank to approximate the generated filters. A 2D MAP detector is then designed to minimize detection errors. With the assumption of known feature, the resulting detector depends only on the filter bank, and not on the noise. Relaxing this assumption yields a detection algorithm that is noise dependent and computationally intensive. The framework is applied to edge detection in a noisy environment, and the results indicate efficient detection. Moreover the 2D MAP can find feature end-points by direct processing of the image. This is unlike conventional methods where edges need to be first detected and then processed to locate the corners. Examples are presented to demonstrate the algorithm. ©2005 Copyright SPIE - The International Society for Optical Engineering.