Hybrid Soft Computing for Image Segmentation
Book information
Description
The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty artificial neural networks, usually applied for machine cognition, learning, and recognition and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.
Similar books
Hybrid Soft Computing for Multilevel Image and Data Segmentation
2016 · PDF
Biomedical Visualisation: Volume 8
2020 · PDF
Recent Developments and the New Direction in Soft-Computing Foundations and Applications: Selected Papers from the 7th World Conference on Soft Computing, May 29–31, 2018, Baku, Azerbaijan
2021 · PDF
Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part II
2020 · PDF
Mind and the Cosmic Order: How the Mind Creates the Features & Structure of All Things, and Why this Insight Transforms Physics
2021 · PDF
Digital Libraries for Open Knowledge: 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25–27, 2020, Proceedings
2020 · PDF
Pioneers of Color Science
2020 · PDF
Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020: Proceedings of the 11th Annual Meeting of the BICA Society
2021 · PDF