Matching Hierarchical Structures Using Association Graphs
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Inexact Multisubgraph Matching Using Graph Eigenspace and Clustering Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Generic Model Abstraction from Examples
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A shock grammar for recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
View-Based 3-D Object Recognition using Shock Graphs
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Shock Graphs and Shape Matching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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Shock graphs have emerged as a powerful generic 2-D shape representation. However, most approaches typically assume that the silhouette has been correctly segmented. In this paper, we present a framework for shock graph-based object recognition in less contrived scenes. The approach consists of two steps, beginning with the construction of a region adjacency graph pyramid. For a given region, we traverse this scale-space, using a model shock graph hypothesis to guide a region grouping process that strengthens the hypothesis. The result represents the best subset of regions, spanning different scales, that matches a given object model. In the second step, the correspondence between the region and model shock graphs is used to initialize an active skeleton that includes a shock graph-based energy term. This allows the skeleton to adapt to the image data while still adhering to a qualitative shape model. Together, the two components provide a coarse-to-fine, model-based segmentation/recognition framework.