Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/2321
Title: User­ Vs. Self-tuning Optimization: A Case Study on Image Registration
Authors: Santamaria, J.
Rivero-Cejudo, M.L.
Abstract: In the last few years, there has been an increased interest in providing new soft computing (SC) algorithms, e.g., evolutionary algorithms (EAs), in which it is not needed the tuning of the control parameters. Usually, this new approach is named self-tuned and several models have been proposed to date. In our study, we aim to, firstly, describe how self-tuning EAs work and, secondly, provide a computational comparison of algorithms dealing with a specific computer vision (CV) task, named image registration (IR), as baseline. In particular, the development of automated IR methods is a well-known issue within the CS field and it was largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. Then, in this contribution it is provided a comprehensive analysis of the self-tuning approach by means of the experimental comparison of several EA-based IR algorithms proposed in the state-of-the-art.
Keywords: Computer vision
Image registration
Softcomputing
Issue Date: 3-Feb-2022
Publisher: Taylor & Francis Group. Apple Academic Press
ISBN: 9781003186885
Citation: Santamaría J., Rivero-Cejudo M.L.;. User­ Vs. Self-tuning Optimization: A Case Study on Image Registration. Applied Soft Computing: Techniques and Applications. Eds. Samarjeet Borah and Ranjit Panigrahi. Apple Academic Press – CRC Press. ISBN-978-1-77463-029-7, 2022. https://doi.org/10.1201/9781003186885
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