title: Bivariate gamma distributions for image registration and change detection.
abstract: This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors.
authors.affiliation: IRIT/ENSEEIHT/T6SA, 31071 Toulouse cedex 7, France. florent.chatelain@enseeiht.fr
authors.forename: Florent
authors.initials: F
authors.lastname: Chatelain
authors.forename: Jean-Yves
authors.initials: JY
authors.lastname: Tourneret
authors.forename: Jordi
authors.initials: J
authors.lastname: Inglada
authors.forename: André
authors.initials: A
authors.lastname: Ferrari
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D016008:Statistical Distributions; D013382:Subtraction Technique