DocumentCode
3311969
Title
Indexing visual representations through the complexity map
Author
Dubuc, Benoit ; Zucker, Steven W.
Author_Institution
McGill Res. Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada
fYear
1995
fDate
20-23 Jun 1995
Firstpage
142
Lastpage
149
Abstract
In differential geometry curves are characterized as mappings from an interval to the plane. In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). The difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. Note that the map defining such curves is unknown, as is the proper way to represent them. We propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. The theory is applied to the problem of perceptual grouping
Keywords
computational complexity; computer vision; differential geometry; visual databases; Hausdorff space; complexity map; computational vision; countability properties; differential geometry; formal complexity theory; perceptual grouping; visual representations indexing; Area measurement; Complexity theory; Computer vision; Detectors; Geometry; Hair; Image edge detection; Indexing; Length measurement; Topology;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision, 1995. Proceedings., Fifth International Conference on
Conference_Location
Cambridge, MA
Print_ISBN
0-8186-7042-8
Type
conf
DOI
10.1109/ICCV.1995.466794
Filename
466794
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