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208
Voted
CVPR
2011
IEEE
15 years 3 months ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...
207
Voted
CVPR
1998
IEEE
16 years 9 months ago
Histogram Model for 3D Textures
Image texture can arise not only from surface albedo variations (2D texture) but also from surface height variations (3D texture). Since the appearance of 3D texture depends on th...
Kristin J. Dana, Shree K. Nayar
216
Voted
CVPR
2005
IEEE
16 years 9 months ago
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semisupervised manner: the model is learnt from example ...
Robert Fergus, Pietro Perona, Andrew Zisserman
CVPR
2007
IEEE
16 years 9 months ago
3D Probabilistic Feature Point Model for Object Detection and Recognition
This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D...
Sami Romdhani, Thomas Vetter
ECCV
2002
Springer
16 years 8 months ago
Dynamic Trees: Learning to Model Outdoor Scenes
Abstract. This paper considers the dynamic tree (DT) model, first introduced in [1]. A dynamic tree specifies a prior over structures of trees, each of which is a forest of one or ...
Nicholas J. Adams, Christopher K. I. Williams