ABSTRACTS OF PAPERS ACCEPTED FOR PUBLICATION 393

Length Estimators for Digitized Contours. LEO DORST. Department of Applied Physics, Delft Univer- sity of Technology, Delft, The Netherlands. ARNOLD W. M. SMEULDERS. Department of Medical Informatics, Free University, de Boelelaan 1118, Amsterdam, The Netherlands. Received July 15, 1986; accepted March 15, 1987.

The estimation of the length of a continuous curve from discrete data is considered. For ideal straight chaincode strings, optimal estimators are given. Comparisons are performed with known methods and recommendations given. A sampling density vs accuracy trade-off theorem is presented. The applicability to nonstraight strings is discussed. For curves that may be considered to be composed of circular arcs good estimators are found.

A Geometric Approach to Subpixel Registration Accuracy. CARLOS A. BERENSTEIN, LAVEEN N. KANAL, DAVID LAVINE, AND ERIC C. OLSON. L.NK. Corporation, 302 Notley Court, Silver Springs, Mary- land 20904. Received November 11, 1984; accepted March 5, 1987.

A method for estimating the translation offset between a pair of images to subpixel accuracy is described and analyzed. This method assumes the extraction of a digital edge representing a straight line in the image and then computes the best real line which could have given rise to the digital line. The key difference between this method and other approaches to subpixel fitting is that our method uses subpixel line location to estimate a digital model such as the digital line and then fits the digital model to the image while other approaches directly fit a continuous model to the image or an image related object such as a cross correlation matrix. The present paper lays a theoretical foundation for this digital approach. This technique can be applied in a variety of remote sensing and industrial vision problems requiring subpixel registration accuracy.

Object Recognition and Localization via Pose Clustering. GEORGE STOCKMAN. Computer Science Department, Michigan State University, East Lansing, Michigan 48824. Received August 13, 1986; accepted March 5, 1987.

The general paradigm of pose clustering is discussed and compared to other techniques applicable to the problem of object detection. Pose clustering is also called hypothesis accumulation and generalized Hough transform and is characterized by a "parallel" accumulation of low level evidence followed by a maxima or clustering step which selects pose hypotheses with strong support from the set of evidence. Examples are given showing the use of pose clustering in both 2D and 3D problems. Experiments show that the positional accuracy of points placed in the data space by a model pose obtained via clustering is comparable to the positional accuracy of the sensed data from which pose candidates are computed. A specific sensing system is described which yields an accuracy of a few millimeters. Complexity of the pose clustering approach relative to alternative approaches is discussed with reference to conventional computers and massively parallel computers. It is conjectured that the pose clustering approach can produce superior results in real time on a massively parallel machine.

N O T E S

A Width-Independent A Igorithm for Character Skeleton Estimation. R . M . K . SINHA. INRS-Telecom., University of Quebec, 3, Place du Commerce, Nuns' Island, Verdun, Quebec, Canada HDE 1H6. Received March 16, 1986; accepted June 4, 1987.

The paper presents an algorithm for the estimation of a skeleton of thick characters. We directly identify the core pixels of the skeleton forming the core skeletal segments based on labeling of the character boundary with some local properties. The core skeletal pixel is defined as the midpoint of a line segment normal to the boundary pixels. These core skeletal segments are extended and joined systematically, based on certain global properties resulting in the final skeleton. The algorithm is independent of the width of the character and is capable of yielding a skeleton close to our intuitive notion of character shape. The topological description of the character is constructed more or less as a by-product of the skeletonization process. The description forms the basis for character recognition using syntactic methods. The algorithm is well suited for parallel implementation.