DocumentCode
419776
Title
Bernoulli mixture models for binary images
Author
Juan, Alfons ; Vidal, Enrique
Author_Institution
Dept. de Sistemas Inf. y Comput., Univ. Politecnica de Valencia, Spain
Volume
3
fYear
2004
fDate
23-26 Aug. 2004
Firstpage
367
Abstract
Mixture modelling is a hot area in pattern recognition. Although most research in this area has focused on mixtures for continuous data, there are many pattern recognition tasks for which binary or discrete mixtures are better suited. This paper focuses on the use of Bernoulli mixtures for binary data and, in particular, for binary images. Results are reported on a task of handwritten Indian digits.
Keywords
handwritten character recognition; image classification; image representation; Bernoulli mixture models; binary data; binary images; discrete mixtures; handwritten Indian digits; image classification; image representation; pattern recognition; Books; Handwriting recognition; Maximum likelihood estimation; Optical character recognition software; Pattern classification; Pattern recognition; Pixel; Testing; Text categorization; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-2128-2
Type
conf
DOI
10.1109/ICPR.2004.1334543
Filename
1334543
Link To Document