Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Active Monte Carlo recognition
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Modeling 3d objects from stereo views and recognizing them in photographs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper we extend Active Monte Carlo Recognition (AMCR), a recently proposed framework for object recognition. The approach is based on the analogy between mobile robot localization and object recognition. Up to now AMCR was only shown to work for shape recognition on binary images. In this paper, we significantly extend the approach to work on realistic images of real world objects. We accomplish recognition under similarity transforms and even severe non-rigid and non-affine deformations. We show that our approach works on databases with thousands of objects, that it can better discriminate between objects than state-of-the art approaches and that it has significant conceptual advantages over existing approaches: It allows iterative recognition with simultaneous tracking, iteratively guiding attention to discriminative parts, the inclusion of feedback loops, the simultaneous propagation of multiple hypotheses, multiple object recognition and simultaneous segmentation and recognition. While recognition takes place triangular meshes are constructed that precisely define the correspondence between input and prototype object, even in the case of strong non-rigid deformations.