COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING 33, 260-261 (1986)

Abstracts of Papers Accepted for Publication

PAPERS

Computing Visibility PoryBon from an Edge. D. T. LEE AND A. K. LIN. Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois 60201. Received August 26, 1984; revised June 26,1985.

We consider the problem of computing the visible region of a simple polygon from a line segment in the polygon. The region, called the visibility polygon, is the area that can be illuminated by a tubular light source represented as a line segment. We present an U(n log n) algorithm for computing the visibility polygon, where n is the number of vertices of the polygon. The algorithm has been implemented in Pascal and some computer output samples are also included.

Segmentation of Textured Images Using G&bs Random F&!s. HALUK DERIN. Department of Electri- cal and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01003. WILLIAM S. COLE. National Research Council, Ottawa, Canada. Received June 25, 1985.

A new algorithm for the segmentation of textured images is developed by making use of Gibbs random fields. A hierarchical stochastic model is employed to represent textured images. At the higher level, the region formation process, describing different areas of the image, is modeled as a Gibbs random field, or equivalently as a Markov random field. At the lower level, the textures in different regions of the image are modeled also as Gibbs random fields. Based on this hierarchical model, the segmentation algorithm being proposed seeks to obtain the maximum a posteriori estimate of the region process using the textured image data. The maximization is carried out recursively by making use of a dynamic programming formulation. Computational concerns, however, necessitate the implementation of a suboptimal version of the algorithm that tries to maximize a pseudo-likelihood over strips of the image. This is a non-trivial extension of a maximum a posteriori segmentation algorithm for noisy images modeled by Gibbs random fields. The segmentation algorithm is applied on several textured images composed of 2,3 region (texture) types and 2 or 4 level textures, with remarkable success. Numerous examples on the application of the segmentation algorithm are presented for textured images with region processes and textures generated according to a particular Gibbs distribution.

Computation of Geunwtric Properties from the Medkd Axis Tramjom in O(n log n) Time. ANGELA Y. WU. Department of Mathematics, Statistics, and Computer Science, The American University, Washington, D.C. 20016. S. K. BHASKAR AND AZRIEL ROSENFELD. Center for Automation Research, University of Maryland, College Park, Maryland 20742. Received July 5, 1985; revised November 1, 1985.

The digital medial axis transform (MAT) represents an image subset S as the union of maximal upright squares contained in S. Brute-force algorithms for computing geometric properties of S from its MAT require time 0( n*), where n is the number of squares. Over the past few years, however, algorithms have been developed that compute properties for a union of upright rectangles in time 0( n log n), which makes the use of the MAT much more attractive. We review these algorithms and also present efficient algorithms for computing union-of-rectangle representations of derived sets (union, intersection, comple- ment) and for conversion between the union of rectangles and other representations of a subset.

Locating Neuron Bovndarics in EIeetron Micwgrwh Images Using “Primal Sketch”Primitives. PETER G. SELFRIDGE. AT&T Bell Laboratories, Room G-625, Holmdel, New Jersey 07733. Received May 9. 1985; revised June 21, 1985.

260 0734-189X/86 $3.00 Copyright 0 1986 by Academic Press. Inc All rights of reproduction in any form resewed