DocumentCode
2367363
Title
A probabilistic approach for shadows modeling and detection
Author
Bouguila, Nizar ; Ziou, Djemel
Author_Institution
Sherbrooke Univ., Que., Canada
Volume
1
fYear
2005
fDate
11-14 Sept. 2005
Abstract
The performance of a statistical image processing system depends in large part on the accuracy of the probabilistic model used. This paper presents a robust probabilistic mixture model based on the Dirichlet distribution. An unsupervised algorithm based on MML for learning this mixture is given, too. Experimental results involve shadows modeling and its application to shadows detection in images.
Keywords
image processing; statistical analysis; Dirichlet distribution; minimum message length; robust probabilistic mixture model; shadows detection; shadows modeling; statistical image processing system; unsupervised algorithm; Image processing; Machine learning; Machine learning algorithms; Mathematical model; Pattern recognition; Power system modeling; Probability; Remote sensing; Robustness; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 2005. ICIP 2005. IEEE International Conference on
Print_ISBN
0-7803-9134-9
Type
conf
DOI
10.1109/ICIP.2005.1529754
Filename
1529754
Link To Document