DocumentCode
3073529
Title
Curve-like sets, normal complexity, and representation
Author
Dubuc, B. ; Zucker, S.W.
Author_Institution
McGill Res. Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada
Volume
1
fYear
1994
fDate
9-13 Oct 1994
Firstpage
216
Abstract
Proposes a theory of the complexity of curves that is sufficient to separate those which extend along their length (e,g., in one dimension) from those that cover an area (e.g., 2-D). The theory is based on original results in geometric measure theory, and is applied to the problems of (i) perceptual grouping and (ii) physiological interpretation, of axonal arbors in developing neurons
Keywords
differential geometry; axonal arbors; curve-like sets; developing neurons; geometric measure theory; normal complexity; perceptual grouping; physiological interpretation; Area measurement; Computer vision; Extraterrestrial measurements; Geometry; Length measurement; Machine intelligence; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on
Conference_Location
Jerusalem
Print_ISBN
0-8186-6265-4
Type
conf
DOI
10.1109/ICPR.1994.576260
Filename
576260
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