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
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;
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
DOI :
10.1109/ICPR.1994.576260
