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Journal article
The representation and matching of categorical shape
Computer vision and image understanding, v 103(2), pp 139-154
2006
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a framework for categorical shape recognition. The coarse shape of an object is captured by a multiscale blob decomposition, representing the compact and elongated parts of an object at appropriate scales. These parts, in turn, map to nodes in a directed acyclic graph, in which edges encode both semantic relations (parent/child) as well as geometric relations. Given two image descriptions, each represented as a directed acyclic graph, we draw on spectral graph theory to derive a new algorithm for computing node correspondence in the presence of noise and occlusion. In computing correspondence, the similarity of two nodes is a function of their topological (graph) contexts, their geometric (relational) contexts, and their node contents. We demonstrate the approach on the domain of view-based 3-D object recognition.
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Details
- Title
- The representation and matching of categorical shape
- Creators
- Ali Shokoufandeh - Drexel UniversityLars Bretzner - Computational Vision and Active Perception Laboratory, Dept. of Numerical Analysis and Computer Science, KTH, Stockholm, SwedenDiego Macrini - University of TorontoM. Fatih Demirci - Department of Computer Science, Drexel University, Philadelphia, PA 19104, USAClas Jönsson - Computational Vision and Active Perception Laboratory, Dept. of Numerical Analysis and Computer Science, KTH, Stockholm, SwedenSven Dickinson - University of Toronto
- Publication Details
- Computer vision and image understanding, v 103(2), pp 139-154
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000239585800005
- Scopus ID
- 2-s2.0-33746037550
- Other Identifier
- 991019183979204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic