Object recognition through invariant indexing
Object recognition through invariant indexing
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Determining Pose of 3D Objects With Curved Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D object recognition using invariance
Artificial Intelligence - Special volume on computer vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Appearance and Geometric Model Based Recognition
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Object Recognition Using Appearance-Based Parts and Relations
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Eigenfeatures for planar pose measurement of partially occluded objects
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Dealing with occlusions in the eigenspace approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Generic Model Abstraction from Examples
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Using multi-modal 3D contours and their relations for vision and robotics
Journal of Visual Communication and Image Representation
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In [2] a new object representation using appearance-based parts and relations to recognize 3D objects from 2D images, in the presence of occlusion and background clutter, was introduced. Appearance-based parts and relations are defined in terms of closed regions and the union of these regions, respectively. The regions are segmented using the MDL principle, and their appearance is obtained from collection of images and compactly represented by parametric manifolds in the eigenspaces spanned by the parts and the relations. In this paper we introduce the discriminatory power of the proposed features and describe how to use it to organize large databases of objects.