Computational geometry: an introduction
Computational geometry: an introduction
Multiple Resolution Representation and Probabilistic Matching of 2-D Gray-Scale Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
3d object recognition using invariant feature indexing of interpretation tables
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
The graph isomorphism problem: its structural complexity
The graph isomorphism problem: its structural complexity
Mathematical Programming: Series A and B - Special issue: Festschrift in Honor of Philip Wolfe part II: studies in nonlinear programming
International Journal of Computer Vision
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Matching Hierarchical Structures Using Association Graphs
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Shock Graphs and Shape Matching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pedestrian detection and tracking in infrared imagery using shape and appearance
Computer Vision and Image Understanding
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The management of large databases of hierarchical (e.g., multi-scale or multilevel) image features is a common problem in object recognition. Such structures are often represented as trees or directed acyclic graphs (DAGs), where nodes represent image feature abstractions and arcs represent spatial relations, mappings across resolution levels, component parts, etc. Object recognition consists of two processes: indexing and verification. In the indexing process, a collection of one or more extracted image features belonging to an object is used to select, from a large database of object models, a small set of candidates likely to contain the object. Given this relatively small set of candidates, a verification, or matching procedure is used to select the most promising candidate. Such matching problems can be formulated as largest isomorphic subgraph or largest isomorphic subtree problems, for which a wealth of literature exists in the graph algorithms community. However, the nature of the vision instantiation of this problem often precludes the direct application of these methods. Due to occlusion and noise, no significant isomorphisms may exists between two graphs or trees. In this paper, we review our application of spectral encoding of a graph for indexing to large database of image features represented as DAGs. We will also review a more general class of matching methods, called bipartite matching, to two problems in object recognition.