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CVPR
2008
IEEE
14 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 ...
ECCV
1998
Springer
14 years 9 months ago
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial con guration. We introduce a simpli ed model of a deforma...
Michael C. Burl, Markus Weber, Pietro Perona
CLOR
2006
13 years 11 months ago
Object Recognition in the Geometric Era: A Retrospective
Recent advances in object recognition have emphasized the integration of intensity-derived features such as affine patches with associated geometric constraints leading to impressi...
Joseph L. Mundy
ICPR
2002
IEEE
14 years 8 months ago
3-D Modeling of an Outdoor Scene by Multi-Baseline Stereo Using a Long Sequence of Images
Three-dimensional (3-D) models of outdoor scenes are widely used for object recognition, navigation, mixed reality, and so on. Because such models are often made manually with hig...
Tomokazu Sato, Masayuki Kanbara, Naokazu Yokoya, H...
ICCV
1998
IEEE
14 years 9 months ago
Modeling Geometric Structure and Illumination Variation of a Scene from Real Images
We present in this paper a system which automatically builds, from real images, a scene model containing both 3D geometric information of the scene structure and its photometric...
Zhengyou Zhang