A shape representation for computer vision based on differential topology

We describe a shape representation for use in computer vision, after a brief review of shape representation and object recognition in general. Our shape representation is based on graph structures derived from level sets whose characteristics are understood from differential topology, particularly s...

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Bibliographic Details
Published inBioSystems Vol. 34; no. 1-3; pp. 197 - 224
Main Author Blicher, A.Peter
Format Journal Article
LanguageEnglish
Published Ireland Elsevier B.V 1995
Subjects
Artificial Intelligence
Computer Simulation
Computer vision
Computers
Differential topology
Geometric modeling
Humans
Image understanding
Mathematics
Models, Biological
Pattern recognition
Pattern Recognition, Visual
Perception
Shape recognition
Vision, Ocular
Computer vision
Geometric modeling
Differential topology
Image understanding
Perception
Pattern recognition
Shape recognition
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Summary:We describe a shape representation for use in computer vision, after a brief review of shape representation and object recognition in general. Our shape representation is based on graph structures derived from level sets whose characteristics are understood from differential topology, particularly singularity theory. This leads to a representation which is both stable and whose changes under deformation are simple. The latter allows smoothing in the representation domain (‘symbolic smoothing’), which in turn can be used for coarse-to-fine strategies, or as a discrete analog of scale space. Essentially the same representation applies to an object embedded in 3-dimensional space as to one in the plane, and likewise for a 3D object and its silhouette. We suggest how this can be used for recognition.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
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ObjectType-Review-1
ISSN:0303-2647
1872-8324
DOI:10.1016/0303-2647(94)01447-F