Computational geometry: an introduction
Computational geometry: an introduction
Structural Stereopsis for 3-D Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
The algebraic eigenvalue problem
The algebraic eigenvalue problem
Recognition and Shape Synthesis of 3-D Objects Based on Attributed Hypergraphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Object Recognition Using Bipartite Matching Embedded in Discrete Relaxation
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
From volumes to views: an approach to 3-D object recognition
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
International Journal of Computer Vision
Mathematical Programming: Series A and B - Special issue: Festschrift in Honor of Philip Wolfe part II: studies in nonlinear programming
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
International Journal of Computer Vision
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning to Form Large Groups of Salient Image Features
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Using Spectral Features for Modelbase Partitioning
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Spectral Methods for View-Based 3-D Object Recognition Using Silhouettes
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Inexact Multisubgraph Matching Using Graph Eigenspace and Clustering Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph Matching Based on Node Signatures
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Object categorization using bone graphs
Computer Vision and Image Understanding
Skeleton comparisons: the junction neighbourhood histogram
Proceedings of the 11th ACM symposium on Document engineering
From exact to approximate maximum common subgraph
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Hi-index | 0.00 |
Hierarchical image structures are abundant in computer vision, and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a unified framework for both indexing and matching such structures. First, we describe an indexing mechanism that maps the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel eigenvalue characterization of a DAG, this topological signature allows us to efficiently retrieve a small set of candidates from a database of models. To accommodate occlusion and local deformation, local evidence is accumulated in each of the DAG's topological subspaces. Given a small set of candidate models, we will next describe a matching algorithm that exploits this same topological signature to compute, in the presence of noise and occlusion, the largest isomorphic subgraph between the image structure and the candidate model structure which, in turn, yields a measure of similarity which can be used to rank the candidates. We demonstrate the approach with a series of indexing and matching experiments in the domains of 2-D and (view-based) 3-D generic object recognition.