2006/03/01 by Charles Bordenave, Yann Gousseau, François Roueff · 1 citation
Computer Science · #Digital Image Processing Techniques #Image Retrieval and Classification Techniques #Medical Image Segmentation Techniques
paper · pdf · doi:10.1239/aap/1143936138
crossref issued 2006/03/01 · crossref published 2006/03/01 · crossref published-print 2006/03/01 · openalex publication_date 2006/03/01 · crossref created 2006/04/04 · crossref published-online 2016/07/01 · crossref deposited 2019/05/02 · openalex created_date 2025/10/10 · crossref indexed 2026/07/29 · openalex updated_date 2026/07/29
In this article, we study a particular example of general random tessellation, the dead leaves model . This model, first studied by the mathematical morphology school, is defined as a sequential superimposition of random closed sets, and provides the natural tool to study the occlusion phenomenon, an essential ingredient in the formation of visual images. We generalize certain results of G. Matheron and, in particular, compute the probability of n compact sets being included in visible parts . This result characterizes the distribution of the boundary of the dead leaves tessellation.