Metaheuristic Algorithms for Image Segmentation: Theory and Applications
The field of image processing is constantly changing due to the extensive integration of cameras in many devices. For example, nowadays smartphones and cars have embedded cameras, and the images must be more accurately analyzed. In the automatic understanding of digital images, some crucial pre-processing steps are applied, like the image segmentation. The incorporation of artificial intelligence to assist the automatic processing of images includes the use of metaheuristics. Metaheuristic algorithms have been implemented in different fields of science and technology as the demand for new methods designed to solve complex optimization problems grows. I will present a study of the most important methods for image segmentation and how they are extended and improved using metaheuristic algorithms. The selected segmentation approaches are selected because they have been extensively applied to the task of segmentation (especially in thresholding). Furthermore, these approaches have also been implemented using different metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective.
February 17, 2020 (Monday)
Room 205, Building E, Karlovo nám. 13
Diego is an associate professor at the Department of Computer Sciences of the Universidad de Guadalajara in the Centro Universitario de Ciencias Exactas e Ingenierias that is located in Jalisco, Mexico. He is also an invited professor at the Tomsk Polytechnic University in Russia for the academic year 2016 – 2017. He works in the research group of Evolutionary Computation and Machine Learning and his primary activity is research in Computer Science, especially in the fields of image processing, soft computing and particularly in metaheuristic algorithms.