Abstract
This work deals with the estimation of mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be generalized when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. The authors propose some methods of estimation of such mixtures based on expectation-maximization (EM), and iterative conditional estimation (ICE) algorithms. The set of families of distributions is assumed to lie in Pearson's system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.