DocumentCode
342584
Title
Combining geometric invariants with fuzzy clustering for object recognition
Author
Walker, Ellen L.
Author_Institution
Dept. of Math. Sci., Hiram Coll., OH, USA
fYear
1999
fDate
36342
Firstpage
571
Lastpage
574
Abstract
Object recognition is the process of identifying the types and locations of objects in the image. Earlier work has shown the desirability of using fuzzy compatibility for local feature correspondence and fuzzy clustering for pose estimation of two dimensional objects. The paper extends the methodology to images of three dimensional objects by applying geometric invariants, specifically the cross ratio of four collinear points. The recognition process is divided into three subtasks: local feature correspondence, object identification, and pose determination. Algorithms are described for each subtask
Keywords
computational geometry; fuzzy set theory; object recognition; pattern clustering; collinear points; cross ratio; fuzzy clustering; fuzzy compatibility; geometric invariants; local feature correspondence; object identification; object recognition; pose determination; pose estimation; recognition process; three dimensional objects; two dimensional objects; Cameras; Clustering algorithms; Computer vision; Educational institutions; Image databases; Image segmentation; Navigation; Object recognition; Robots; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location
New York, NY
Print_ISBN
0-7803-5211-4
Type
conf
DOI
10.1109/NAFIPS.1999.781758
Filename
781758
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