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» Probability Models for High Dynamic Range Imaging
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CVPR
2003
IEEE
15 years 11 months ago
Face Recognition Under Variable Lighting using Harmonic Image Exemplars
We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3...
Lei Zhang 0002, Dimitris Samaras
ISIPTA
1999
IEEE
15 years 2 months ago
Modeling Ellsberg's Paradox in Vague-Vague Cases
We explore a generalization of Ellsberg's paradox (2color scenario) to the Vague-Vague (V-V) case, in which neither of the probabilities (urns) is specified precisely, but on...
Karen M. Kramer, David V. Budescu
ICASSP
2011
IEEE
14 years 1 months ago
Quadratic optimization based small scale details extraction
In many image processing problems, it is required to extract small scale details from an image or a set of images. In this paper, we introduce a new framework for extracting small...
Zhengguo Li, Jinghong Zheng, Chuohao Yeo, Susanto ...
ICIP
2007
IEEE
15 years 11 months ago
Monocular Tracking 3D People By Gaussian Process Spatio-Temporal Variable Model
Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temp...
Junbiao Pang, Laiyun Qing, Qingming Huang, Shuqian...
ICIP
2009
IEEE
15 years 9 months ago
A Markov Random Field Model for Extracting Near-Circular Shapes
We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We...
Tamas Blaskovics, Zoltan Kato, and Ian Jermyn