DocumentCode
2974933
Title
Higher-order statistical models of visual images
Author
Simoncelli, Eero P.
Author_Institution
Center for Neural Sci., New York Univ., NY, USA
fYear
1999
fDate
1999
Firstpage
54
Lastpage
57
Abstract
This paper examines the empirical densities of natural photographic images, and shows that although they are highly non-Gaussian, they are quite regular and may be described using fairly simple parameterized density models. Two such models are described, and their ability to account for image content is demonstrated
Keywords
higher order statistics; image processing; photography; wavelet transforms; computer graphics; computer vision; empirical densities; higher-order statistical models; image processing; natural photographic images; parameterized density models; visual images; wavelet domain analysis; Application software; Bandwidth; Entropy; Frequency; Histograms; Identity-based encryption; Independent component analysis; Mathematical model; Principal component analysis; Statistics;
fLanguage
English
Publisher
ieee
Conference_Titel
Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Conference_Location
Caesarea
Print_ISBN
0-7695-0140-0
Type
conf
DOI
10.1109/HOST.1999.778691
Filename
778691
Link To Document