Visual learning by integrating descriptive and generative methods

Proceedings of the IEEE International Conference on Computer Vision

Volumn 1, Issue , 2001, Pages 370-377

  Guo, C ^a   Zhu, S C ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; IMAGE CODING; IMAGE ENHANCEMENT; MARKOV PROCESSES; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS;

DESCRIPTIVE LEARNING; GENERATIVE LEARNING; VISUAL LEARNING;

COMPUTER VISION;

EID: 0034857138     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Maximum likelihood from incomplete data via the EM algorithm
2. Transformed component analysis: Joint estimation of spatial transforms and image components
3. Reversible jump Markov chain Monte Carlo computation and bayesian model determination
4. A stochastic approximation algorithm with MCMC method for incomplete data estimation problems
5. Texton, the fundamental elements in preattentive vision and perception for textures
6. Detecting, localizing and grouping repeated scene elements from an image
7. Recognizing surface using three-dimensional textons
8. Emergence of simple-cell receptive field properties by learning a sparse code for natural images
9. A unifying review of linear gaussian models
10. A parametric texture model based on joint statistics of complex wavelet coefficients
11. Minimax entropy principle and its application to texture modeling
12. Conceptualization and modeling of visual patterns