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CVPR
2007
IEEE
16 years 8 months ago
What makes a good model of natural images?
Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non G...
Yair Weiss, William T. Freeman
NIPS
2008
15 years 8 months ago
Structure Learning in Human Sequential Decision-Making
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Daniel Acuña, Paul R. Schrater
AAAI
2012
13 years 9 months ago
Supervised Probabilistic Robust Embedding with Sparse Noise
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...
Yu Zhang, Dit-Yan Yeung, Eric P. Xing
CVPR
2009
IEEE
17 years 1 months ago
Global Optimization for Alignment of Generalized Shapes
In this paper, we introduce a novel algorithm to solve global shape registration problems. We use gray-scale “images” to represent source shapes, and propose a novel twocompo...
Hongsheng Li (Lehigh University), Tian Shen (Lehig...
CVPR
2003
IEEE
16 years 8 months ago
Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field
This paper presents a generative model based approach to man-made structure detection in 2D natural images. The proposed approach uses a causal multiscale random field suggested i...
Sanjiv Kumar, Martial Hebert