Title :
Digital geometric invariance and shape representation
Author :
Gross, Ari ; Latecki, Longin
Author_Institution :
Graduate Center, City Univ. of New York, Flushing, NY, USA
Abstract :
In this paper, we present conditions which guarantee that a realistic digitization process preserves the qualitative differential geometry of the object boundary, such as convexity and inflection. This is possible since very few digital boundary patterns are shown to be realizable, and each such digital pattern has a well-defined geometric interpretation with respect to tangent direction. Using the set of realizable boundary patterns, we can recover geometric properties of the digitized object boundary, such as convexity and inflection. In addition, since all the realizable patterns are known, any other pattern can be labeled as either noise or a boundary discontinuity. Since each of these patterns has a well-defined tangent span, an adjacency graph can be generated from the patterns and this graph can be used to recursively generate the set of all possible digital boundary curves. The digitization process used in this paper is equivalent to setting a very low threshold value on the sensor output
Keywords :
computer vision; differential geometry; image representation; adjacency graph; convexity; digital geometric invariance; inflection; object boundary; qualitative differential geometry; realistic digitization process; shape representation; Computational geometry; Computer science; Computer vision; Educational institutions; Image sensors; Machine vision; Parametric statistics; Shape; Solid modeling; USA Councils;
Conference_Titel :
Computer Vision, 1995. Proceedings., International Symposium on
Conference_Location :
Coral Gables, FL
Print_ISBN :
0-8186-7190-4
DOI :
10.1109/ISCV.1995.476988