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
Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection
Mario Versaci
;Francesco Carlo Morabito
2022-01-01
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 worksI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
