Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
3d object recognition using invariant feature indexing of interpretation tables
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Ligature instabilities in the perceptual organization of shape
Computer Vision and Image Understanding
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Unified Framework for Indexing and Matching Hierarchical Shape Structures
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
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
Journal of Cognitive Neuroscience
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The shockg raph is an emerging shape representation for object recognition, in which a 2-D silhouette is decomposed into a set of qualitative parts, captured in a directed acyclic graph. Although a number of approaches have been proposed for shock graph matching, these approaches do not address the equally important indexing problem. We extend our previous workin both shock graph matching and hierarchical structure indexing to propose the first unified framework for view-based 3-D object recognition using shock graphs. The heart of the framework is an improved spectral characterization of shock graph structure that not only drives a powerful indexing mechanism (to retrieve similar candidates from a large database), but also drives a matching algorithm that can accommodate noise and occlusion. We describe the components of our system and evaluate its performance using both unoccluded and occluded queries. The large set of recognition trials (over 25,000) from a large database (over 1400 views) represents one of the most ambitious shockg raph-based recognition experiments conducted to date. This paper represents an expanded version of [12].