Probabilistic Models of Appearance for 3-D Object Recognition
International Journal of Computer Vision
A Fragment-Based Approach to Object Representation and Classification
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Generic Model Abstraction from Examples
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Object Classification Using a Fragment-Based Representation
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Video retrieval using spatio-temporal descriptors
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Generic Model Abstraction from Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning semantic object parts for object categorization
Image and Vision Computing
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical non-parametric method for capturing non-rigid deformations
Image and Vision Computing
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
Cue integration through discriminative accumulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The optimal distance measure for object detection
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Object recognition using local descriptors: a comparison
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We describe an appearance-based object recognition system using a keyed, multi-level context representation reminiscent of certain aspects of cubist art. Specifically, we utilize distinctive intermediate-level features in this case automatically extracted 2-Dboundary fragments, as keys, which are then verified within a local context, and assembled within a loose global context to evoke an overall percept. This system demonstrates good recognition of a variety of 3-D shapes, ranging from sports cars and fighter planes to snakes and lizards with full orthographic invariance. We report the results of large-scale tests, involving over 2000 separate test images, that evaluate performance with increasing number of items inthe database, in the presence of clutter, background change, and occlusion, and also the results of some generic classification experiments where the system is tested on objects never previously seen or modeled. To our knowledge, the results we report are the best in the literature for full-sphere tests of general shapes with occlusion and clutter resistance.