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173
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MVA
1990
101views Computer Vision» more  MVA 1990»
15 years 8 months ago
Recognition of Parametrised Models from 3D Data
This paper describes work done as part of the Oxford AGV (Autonomous Guided Vehicle) project [2] towards recognition of classes of objects to be encountered in a factory environme...
Ian D. Reid
ECCV
2006
Springer
16 years 8 months ago
Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition
Abstract. In this paper we investigate a new method of learning partbased models for visual object recognition, from training data that only provides information about class member...
David J. Crandall, Daniel P. Huttenlocher
176
Voted
CVPR
2008
IEEE
16 years 9 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
209
Voted
CVPR
2010
IEEE
16 years 15 days ago
Learning 3D Action Models from a few 2D videos for View Invariant Action Recognition
Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amount...
Pradeep Natarajan, Vivek Singh, Ram Nevatia
179
Voted
CVPR
2009
IEEE
15 years 10 months ago
Shape-based object recognition in videos using 3D synthetic object models
In this paper we address the problem of recognizing moving objects in videos by utilizing synthetic 3D models. We use only the silhouette space of the synthetic models making thus...
Alexander Toshev, Ameesh Makadia, Kostas Daniilidi...