Welcome to the InfoSci Platform
Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection

Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection

Mario Versaci, Francesco Carlo Morabito
Copyright: © 2022 |Pages: 52
ISBN13: 9781799886860|ISBN10: 1799886867|EISBN13: 9781799886877
DOI: 10.4018/978-1-7998-8686-0.ch008
Cite Chapter Cite Chapter

MLA

Versaci, Mario, and Francesco Carlo Morabito. "Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection." Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, edited by Maki K. Habib, IGI Global, 2022, pp. 160-211. https://doi.org/10.4018/978-1-7998-8686-0.ch008

APA

Versaci, M. & Morabito, F. C. (2022). Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection. In M. Habib (Ed.), Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning (pp. 160-211). IGI Global. https://doi.org/10.4018/978-1-7998-8686-0.ch008

Chicago

Versaci, Mario, and Francesco Carlo Morabito. "Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection." In Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, edited by Maki K. Habib, 160-211. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8686-0.ch008

Export Reference

Mendeley
Favorite

Abstract

In the AI framework, edge detection is an important task especially when images are affected by uncertainties and/or inaccuracies. Thus, usual edge detectors are unsuitable, so it is necessary to exploit fuzzy tools as Versaci-Morabito edge detector proposing a procedure to adaptively construct fuzzy membership functions. In this chapter, the authors reformulate this approach exploiting a new formulation for adaptively fuzzy membership functions but characterized by a more reduced computational load making the approach more attractive for any real-time applications. Furthermore, the chapter provides new mathematical results not yet proven in previous works.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.