Adaptive multiscale feature extraction from range data

Adaptive multiscale feature extraction from range data

Abstracts of Papers Accepted for Publication PAPERS B-Spline Cwves and Surfaces Viewed as Digital Filters. ARDESHIR GOSHTA~BY AND FUHIJA CHENG. Dep...

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Abstracts

of Papers Accepted

for Publication

PAPERS B-Spline Cwves and Surfaces Viewed as Digital Filters. ARDESHIR GOSHTA~BY AND FUHIJA CHENG. Department of Computer Science, University of Kentucky, Lexington, Kentucky 40506. BRSAN A. BARSKY. Computer Science Division-EECS, University of California Berkeley, Berkeley, California 94720. Received March 29, 1988; accepted October 7, 1988. In this paper we show that B-spline curves and surfaces can be viewed as digital filters. Viewing B-spline problems as digital filters allows one to predict some properties of the generated curves and surfaces. We find that even-order B-splines and odd-order B-splines behave differently when used in curve and surface interpolation. Even-order B-splines generate smoother curves and surfaces than do odd-order B-splines.

Aakrptive Multiscale Feature Extraction from Range Data. B. PARVIN AND G. MEDIONI. Institute of Robotics and Intelligent Systems, Department of Electrical Engineering, Powel Hall, 233, University of Southern California, Los Angeles, California 90089-0272. Received February 17, 1988; accepted October 7. 1988. In this paper, we present a method to extract meaningful features from range images which introduces some novel ideas: First, our primitives are a richer set than the usual one, as we not only extract high frequency events, which correspond to jump boundaries and sharp creases, but also low frequency events such as smooth ridges and ravines. These last features are generally discarded as they tend to hide in noise; however, they provide a coarse description of the shape, and we believe they may help in inferring volumetric descriptions. The method works at multiple scales to improve reliability, and we have designed a control strategy to automatically select the most appropriate mask size. We present a result on images of different levels of complexity from three different sensors.

Finite Topohqy as Applied to Image Analysis. V. A. KOVALEVSKY. Central Information Processing, Kurstrasse 33, 1086 Berlin, German Democratic 10, 1988; accepted September 7, 1988.

Institute of Cybernetics and Republic. Received August

The notion of a cellular complex which is well known in the topology is applied to describe the structure of images. It is shown that the topology of cellular complexes is the only possible topology of finite sets. Under this topology no contradictions or paradoxes arise when defining connected subsets and their boundaries. Ways of encoding images as cellular complexes are discussed. The process of image segmentation is considered as splitting (in the topological sense) a cellular complex into blocks of cells. The notion of a cell list is introduced as a precise and compact data structure for encoding segmented images. Some applications of this data structure to the image analysis are demonstrated.

The Same-Object Problem for Polyltedal York 14853 and McGill University, September 16. 1988.

So/i& Montreal,

MICHAEL Quebec,

KARASICK. Canada.

Cornell Received

University, Ithaca, New, March 8, 1988; revised

The problem of deciding if two representations describe the same object arises in many applications in solid modeling. Using constructive solid geometry, the best known algorithm for the problem requires time O(n4), where n bounds the number of surface/surface interactions of the symmetric set-difference of the two solids. By using a new canonical boundary representation for polyhedral solids, the 266 0734-189X/89

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