Skip to Main Content
The problem considered in this paper is the estimation of highly variable object boundaries in noisy images. Boundaries may be those of a tank in an IR image, a spinal canal in a CAT scan, a cloud in a visible light image, etc. Or they may be internal to an object such as the boundary between a spherical surface and a cylindrical surface in a manufactured object. The focus of the paper is on parallel multiple-window boundary estimation algorithms. Here the image field is parti-tioned into an array of rectangular windows, and boundary finders are run simultaneously within the windows. The boundary segments found within the windows are then seamed together to obtain meaningful global boundaries. The entire procedure is treated within a maximum likelihood estimation framework that we have developed for boundary finding. Although our multiple-window estimation approach can be used with a number of local boundary finding algorithms, we concen-trate on one which is based on dynamic programming and will produce the true maximum likelihood boundary. Some theoretical considera-tions for boundary model design and boundary-finding runtime are covered. Included is the use of a low computational cost F-test for test-ing whether a window contains a boundary, and an analytical treatment which shows that use of coarse pixels with a chi-square test or an F-test improves the probability of correctly recognizing whether a boundary is present in a window.