Abstract
Morphological granulometries are frequently used as descriptors of granularity, or texture, within a binary image. In this paper, we study the problem of estimating the (discrete) size distribution and size density of a random binary image by means of empirical, as well as, Monte Carlo estimators. Theoretical and experimental results demonstrate superiority of the Monte Carlo estimation approach, and suitability of mathematical morphology in studying important properties of a particular binary random image model, widely known as a Markov random field model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
