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Bivariate gamma distributions for image registration and change detection
Florent Chatelain1, Jean-Yves Tourneret, Jordi Inglada
1IRIT/ENSEEIHT/T6SA, 31071 Toulouse cedex 7, France. florent.chatelain@enseeiht.fr
This study explores bivariate gamma distributions for image registration and change detection. Results show these distributions are effective for developing new algorithms in these fields.
Area of Science:
- Statistics
- Image Processing
- Computer Vision
Background:
- Image registration and change detection are crucial in various applications.
- Traditional methods may have limitations in handling complex image data.
- Bivariate gamma distributions offer a flexible statistical framework.
Purpose of the Study:
- To evaluate bivariate gamma distributions for image registration and change detection.
- To develop and assess parameter estimation methods for these distributions.
- To investigate the properties of mutual information within this framework.
Main Methods:
- Parameter estimation using Maximum Likelihood and Method of Moments.
- Performance comparison based on Mean Square Errors and Asymptotic Variances.
- Analysis of Mutual Information properties for bivariate Gamma distributions.
Main Results:
- Both Maximum Likelihood and Method of Moments provide effective parameter estimators.
- Bivariate gamma distributions demonstrate strong performance in simulations.
- Encouraging results on synthetic and real image data.
Conclusions:
- Bivariate gamma distributions are promising for image analysis tasks.
- They enable the development of novel image registration algorithms.
- They facilitate the creation of advanced change detection techniques.
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