RT journal article
T1 Segmentation of scanning-transmission electron microscopy images using the ordered median problem
A1 Calvino Gámez, José Juan
A1 López Haro, Miguel
A1 Muñoz Ocaña, Juan Manuel
A1 Puerto, Justo
A1 Rodríguez Chía, Antonio Manuel
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
A2 Estadística e Investigación Operativa
K1 Location
K1 Ordered median function
K1 Segmentation
K1 Clustering
K1 Mixed integer linear programming
AB This paper presents new models for segmentation of 2D and 3D Scanning-Transmission Electron Micro- scope images based on the ordered median function. The main advantage of using this function is its good adaptability to the different types of images to be studied due to the wide range of weight vec- tors that can be cast. Classical segmentation models stand out for their ability to provide a segmentation of the original image very quickly and with low computational burden. However, they do not usually achieve high quality segmentations with a small number of clusters in order to classify the different ele- ments which compose the structure represented in the image. The quality of the segmentation provided by our approach is analysed using different choices of the weight vector in some real instances. More- over, improvements are proposed for the formulations to reduce the computational time needed to solve these problems by taking advantage of the weight vector structure
PB ELSEVIER
SN 0377-2217
YR 2022
FD 2022
LK http://hdl.handle.net/10498/26920
UL http://hdl.handle.net/10498/26920
LA eng
NO The authors have been partially supported by projects PID2020-114594GB-{C21, C22}, PID2020-1130 06RB-I0 0, PID2019- 110 018GA-I0 0, MAT2017-87579-R funded by MCIN/AEI/ 10.13039/50110 0 011033 and project NetmeetData Fundacion BBVA convocatoria 2019. This work has been co-funded by the 2014–2020 ERDF Operational Programme and by the Depart- ment of Economy, Knowledge, Business and University of the Regional Government of Andalusia; projects FEDER-UCA18-106895, FEDER-UCA18-107139, FEDER-US-1256951 and P18-FR-1422.
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026