2016/09/09 by Amin Fehri, Fehri, Amin, Santiago Velasco-Forero +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Enhancement Techniques #Machine Learning (cs.LG) #Machine learning #Medical Image Segmentation Techniques #Selection (genetic algorithm) #Watershed #cs.CV #cs.LG
paper · pdf · open access · doi:10.48550/arxiv.1609.02715
published in HAL (Le Centre pour la Communication Scientifique Directe) (Centre National de la Recherche Scientifique) · in European Conference of Signal Processing (EUSIPCO), 2016, Budapest, Hungary
arxiv created 2016/09/09 · openalex publication_date 2016/09/09 · arxiv updated 2016/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets.