International Journal of Computer Vision
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fingerprint Matching Using an Orientation-Based Minutia Descriptor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Graph-based Object Tracking Using Structural Pattern Recognition
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Feature Correspondence Via Graph Matching: Models and Global Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Local feature-based object recognition methods recognize learned objects by unordered local feature matching followed by verification. However, the matching between unordered feature sets might be ambiguous as the number of objects increases, because multiple similar features can be observed in different objects. In this context, we present a new method for textured object recognition based on relational information between local features. To efficiently reduce ambiguity, we represent objects using the Attributed Relational Graph. Robust object recognition is achieved by the inexact graph matching. Here, we propose a new method for building graphs and define robust attributes for nodes and edges of the graph, which are the most important factors in the graphbased object representation, and also propose a cost function for graph matching. Dependent on the proposed attributes, the proposed framework can be applied to both single-image-based and stereo-image-based object recognition.