2026-04-18T06:09:58Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1012552026-01-23T04:00:05Zcom_2072_1033col_2072_452950

Unsupervised segmentation controlled by morphological contrast ext Marqués Acosta, Fernando Gasull Llampallas, Antoni Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Telecommunication Mathematical morphology Image segmentation Telecomunicació A novel approach for unsupervised image segmentation is described. This approach makes use of a Gaussian pyramid as multiresolution decomposition to analyze images. Compound random fields are used to model images at each resolution. The hierarchical image model is formed by a Strauss process in the lower level and a set of white Gaussian random fields in the upper level. This basic image model is adapted to the data present at each resolution. Segmentations at coarse resolutions are used to guide segmentations at finest resolutions. Segmentation quality is controlled, at each level, by means of morphological tools. The control procedure is based on the residue between the original image and a morphological center transform. This procedure checks whether the current segmentation contains all the relevant regions in the scene. If not, the algorithm introduces seeds into the segmented image in order to detect the new regions. Peer Reviewed Postprint (published version) 1993 Conference report Marques, F., Gasull, A. Unsupervised segmentation controlled by morphological contrast ext. A: IEEE International Conference on Acoustics, Speech and Signal Processing. "ICASSP 1993: IEEE International Conference on Acoustics, Speech and Signal Processing: April 27- 30, 1993". . ICASSP, 1993, p. 517-520. https://hdl.handle.net/2117/101255 10.1109/ICASSP.1993.319736 eng http://ieeexplore.ieee.org/document/319736/ http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access 4 p. application/pdf . ICASSP