You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format. However, this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the Department of Computer Science of the University of Maryland at College Park under terms that include this permission. All other rights are reserved by the author(s).
Adlai Waksman. Azriel Rosenfeld. Sparse, Opaque Three-Dimensional Texture, 2b: Photometry. October 1994.
This paper deals with 3D textures composed of approximately planar texels distributed over a volume of space ("leafy" textures). It studies the gray level histograms of images of such textures under illumination by a compact light source. Simple models c an be used to iescribe the variation of such histograms with light source direction. In fact, the variation If real plant histograms with light source direction resembles that of synthetic histograms generated using a Phong-type reflectance model and a un iform texel orientation model, and Ignoring transmittance, interreflection, and shadows. (Also cross-referenced as CAR-TR-740) Department of Computer Science, University of Maryland, Center for Automation Research, The postscript version of this TR is available from the Center for Automation Research via anonymous ftp at ftp.cfar.umd.edu; or via the WWW at http://www.cfar.umd.edu/CfAR/TRs.
Samir Khuller. Balaji Raghavachari. Azriel Rosenfeld. July 28, 1994.
Localization in Graphs. Navigation can be studied in a graph-structured framework in which the navigating agent (which we shall assume to be a point robot) moves from node to node of a ``graph space''. The robot can locate itself by the presence of distinctively labeled ``landmark'' nodes in the graph space. For a robot navigating in Euclidean space, visual detection of a distinctive landmark provides information about the direction to the landmark, and allows the robot to determine its position by triangulation. On a graph, however, there is neither the concept of direction nor that of visibility. Instead, we shall assume that a robot navigating on a graph can sense the distances to a set of landmarks. Evidently, if the robot knows its distances to a sufficiently large set of landmarks, its position on the graph is uniquely determined. This suggests the following problem: given a graph, what are the fewest number of landmarks needed, and where should they be located, so that the distances to the landmarks uniquely determine the robot's position on the graph? This is actually a classical problem about metric spaces. A minimum set of landmarks which uniquely determine the robot's position is called a ``metric basis'', and the minimum number of landmarks is called the ``metric dimension'' of the graph. In this paper we present some results about this problem. Our main {\em new\/} result is that the metric dimension can be approximated in polynomial time within a factor of $O(\log n)$; we also establish some properties of graphs with metric dimension 2. (Also cross-referenced as UMIACS-TR-94-92) University of Maryland Institute for Advanced Computer Studies, Dept. of Computer Science, Univ. of Maryland,
Azriel Rosenfeld. "Geometric Properties" of Sets of Lines. (Also cross-referenced as CAR-TR-724) July 1994.
Computer Vision Laboratory, When we regard the plane as a set of points, we can define various geometric properties of subsets of the plane connectedness, convexity, area, diameter, etc. It is well known that the plane can also be regarded as a set of lines. This note considers methods of defining sets (or fuzzy sets) of lines in the plane, and of defining (analogs of) "geometric properties" for such sets. Department of Computer Science, University of Maryland, Center for Automation Research,
(Also cross-referenced as CAR-TR-703) February 1994.
Recognition by Functional Parts. Ehud Rivlin. Sven J. Dickinson. Azriel Rosenfeld. Department of Computer Science, University of Maryland, Center for Automation Research, We present an approach to function-based object recognition that reasons about the functionality of an object's intuitive parts. We extend the popular "recognition by parts" shape recognition framework to support "recognition by functional parts", by com bining a set of functional primitives and their relations with a set of abstract volumetric shape primitives and their relations. Previous approaches have relied on more global object features, often ignoring the problem of object segmentation and thereby restricting themselves to range images of unoccluded scenes. We show how these shape primitives and relations can be easily recovered from superquadric ellipsoids which, in turn, can be recovered from either range or intensity images of occluded scenes. Furthermore, the proposed framework supports both unexpected (bottom-up) object recognition and expected (top-down) object recognition. We demonstrate the approach on a simple domain by recognizing a restricted class of hand-tools from 2-D images.
(Also cross-referenced as CAR-TR-698) Image Analysis and Computer Vision: 1993. Azriel Rosenfeld. Center for Automation Research, Department of Computer Science, University of Maryland, January 1994.
This paper presents a bibliography of nearly 1300 references related to computer vision and image analysis, arranged by subject matter. The topics covered include computational techniques; feature detection and segmentation; image analysis; twodimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, such as geometry, graphics, coding and processing, sensors and optical processing, visual perception, neural nets, pattern recognition, and artificial intelligence, as well as on applications. The postscript version of this TR is available from the Center for Automation Research via anonymous ftp at ftp.cfar.umd.edu; or via the WWW at http://www.cfar.umd.edu/CfAR/TRs.
Azriel Rosenfeld. Fuzzy Plane Geometry: Triangles. (Also cross-referenced as CAR-TR-694) November 1993.
A fuzzy triangle T (with a discrete-valued membership function) can be regarded as a nest of parallel-sided triangles Ti with successively higher membership values. Such a nest is determined by its max projections on any two of its "sides". The area (per imeter) of T is a weighted sum of the areas (perimeters) of the Ti's. The side lengths and altitudes of T can also be defined as weighted sums obtained from projections; using these definitions, the perimeter of T is the sum of the side lengths, and the s ide lengths are related to the vertex angles by the Law of Sines, but there is no simple relationship between the area of T and the products of the side lengths and altitudes. Department of Computer Science, University of Maryland, Center for Automation Research,
Last Generated Fri Aug 11 04:01:01 EDT 2000