An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
View-Based Dynamic Object Recognition Based on Human Perception
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Using Temporal Coherence to Build Models of Animals
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Integrating multiple model views for object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Wide-baseline multiple-view correspondences
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Learning 3D object recognition from an unlabelled and unordered training set
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
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This paper proposes an approach for the unsupervised learning of object models from local image feature correspondences. The object models are learned from an unlabeled sequence of training images showing one object after the other. The obtained object models enable the recognition of these objects in cluttered scenes, under occlusion, in-plane rotation and scale change. Maximally stable extremal regions are used as local image features and two different types of descriptors characterising the appearance and shape of the regions allow a robust matching. Experiments with real objects show the recognition performance of the presented approach under various conditions.