Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2011-05-18
Computer Vision and Image Understanding, Volume 115, Issue 7, July 2011, Pages 1011-1022
Computer Science
Computer Vision and Pattern Recognition
Special issue on Graph-Based Representations in Computer Vision
Scientific paper
10.1016/j.cviu.2010.12.009
Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space than does homology. It assigns `quantities' to the chains used in homology to characterize holes of any dimension. Graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. This paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiently compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. An extension to obtain scanning and rotation invariant cocycles is given.
Gonzalez-Diaz Rocio
Iglesias-Ham Mabel
Ion Adrian
Kropatsch Walter G.
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