title: Unsupervised variational image segmentation/classification using a Weibull observation model.
abstract: Studies have shown that the Weibull distribution can model accurately a wide variety of images. Its parameters index a family of distributions which includes the exponential and approximations of the Gaussian and the Raleigh models widely used in image segmentation. This study investigates the Weibull distribution in unsupervised image segmentation and classification by a variational method. The data term of the segmentation functional measures the conformity of the image intensity in each region to a Weibull distribution whose parameters are determined jointly with the segmentation. Minimization of the functional is implemented by active curves via level sets and consists of iterations of two consecutive steps: curve evolution via Euler-Lagrange descent equations and evaluation of the Weibull distribution parameters. Experiments with synthetic and real images are described which verify the validity of method and its implementation.
authors.affiliation: Institut National de la Recherche Scientifique, INRS-EMT, Montréal, QC H5A 1K6, Canada. benayedi@emt.inrs.ca
authors.forename: Ismail
authors.initials: I
authors.lastname: Ben Ayed
authors.forename: Nacera
authors.initials: N
authors.lastname: Hennane
authors.forename: Amar
authors.initials: A
authors.lastname: Mitiche
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016008:Statistical Distributions