84 ABSTRACTS OF PAPERS ACCEPTED FOR PUBLICATION

Curvature Relations in Three-Dimensional Symmetric Axes. LEE R. NACKMAN. Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina 275 14.

Received December 3, 1980; revised February 26, 1981; accepted September 21, 1981

Aspects of symmetric axis geometry in three dimensions are discussed. A notion of radius curvature is defined and a relationship between symmetric axis curvature, radius curvature, and boundary curvature is derived.

Mites (mit-as): A Model-Driven, Iterative Texture Segmentation Algorithm. LARRY S. DAVIS, Computer Science Department, University of Maryland, College Park, Maryland 20742, AND AMAR MITICHE, Department of Computer Sciences, University of Texas, Austin, Texas 78712.

Received February 23, 1981; revised August 17, 1981.

This paper presents a new algorithm for segmentation of images containing textured regions. The algorithm is named MITES, which is an acronym for Fodel-driven, iterative, Texture Segmentation. MITES represents an alternative to the traditional pixel classification approach to texture image segmentation because it makes explicit use of the spatial coherence of uniformly textured regions.

Matching of Featured Objects Using Relational Tables from Stereo Images. J. J. HWANG, ADR Ultra- sound, 2626 S. Roosevelt, Tempe, Arizona 85282 AND E. L. HALL, Electrical Engineering Department, The University of Tennessee, Knoxville, Tennessee 37916.

Received March 5, 1981; revised October 29, 1981

A method for three-dimensional scene matching is presented in this paper. Both the geometric and structural information of the segmented features in two images are used for three-dimensional scene matching. The segmented features such as regions, edge segments and vertices are initially labelled by using a symbol set. Then the structural relationships among these labels in each image are tabulated in a relational table. The consistent labels between two relational tables associated with two given images are searched using a relaxation labelling process. In this process, the matching line equation between the two images is used as a constraint function to remove the ambiguous labels from the two relational tables. This process is applied iteratively until two isomorphic relational tables are deduced. Since the labels in the two isomorphic tables are in one-to-one correspondence, the problem of matching the two images is reduced to a problem of matching regions to regions, edge segments to edge segments and vertices to vertices. Again using the matching line equation as a geometric constraint function, the corresponding points in the two images may be searched in the corresponding edge segments. The three-dimensional object geometry is then computed using the matched corresponding points.

On Cubits: A Suroey., WOLFGANG BOEHM. Institut fuer Angewandte Mathematik der Technischen Universitaet Braunschweig, West Germany.

Received March 19, 1981; revised September 10, 1981.

Standard cubits, i.e., nonrational cubits, are the simplest twisted curves-hence their considerable importance for CAGD. Rational cubits allow modification of their Brllness even when the end tangents are kept fixed; this is the reason why they are occasionally preferred to standard cubits in CAGD. We shall point out connections between the various representations and their underlying geometric properties. This should serve as an easy and intuitive introduction and help the potential user choose a suitable representation. Some new algorithms can be obtained in a straightforward way.