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CVPR
2007
IEEE
16 years 5 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
126
Voted
ICIP
2006
IEEE
16 years 5 months ago
Denoising Archival Films using a Learned Bayesian Model
We develop a Bayesian model of digitized archival films and use this for denoising, or more specifically de-graining, individual frames. In contrast to previous approaches our mod...
Teodor Mihai Moldovan, Stefan Roth, Michael J. Bla...
156
Voted
PAMI
2002
112views more  PAMI 2002»
15 years 3 months ago
Recognizing Handwritten Digits Using Hierarchical Products of Experts
The product of experts learning procedure [1] can discover a set of stochastic binary features that constitute a nonlinear generative model of handwritten images of digits. The qua...
Guy Mayraz, Geoffrey E. Hinton
ICDAR
2009
IEEE
15 years 10 months ago
Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation
This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vecto...
Yanjie Wang, Xiabi Liu, Yunde Jia
132
Voted
CVPR
2007
IEEE
16 years 5 months ago
An Exemplar Model for Learning Object Classes
We introduce an exemplar model that can learn and generate a region of interest around class instances in a training set, given only a set of images containing the visual class. T...
Ondrej Chum, Andrew Zisserman