Adaptive binarization based on fuzzy integrals
Abstract
Adaptive binarization methodologies threshold the intensity of the pixels with respect to adjacent pixels exploiting the integral images. In turn, the integral images are generally computed optimally using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient design of a modified SAT for fuzzy integrals. We define this new methodology as FLAT (Fuzzy Local Adaptive Thresholding). The experimental results show that the proposed methodology have produced an image quality thresholding often better than traditional algorithms and saliency neural networks. We propose a new generalization of the Sugeno and CF 1,2 integrals to improve existing results with an efficient integral image computation. Therefore, these new generalized fuzzy integrals can be used as a tool for grayscale processing in real-time and deep-learning applications. Index Terms: Image Thresholding, Image Processing, Fuzzy Integrals, Aggregation Functions
Keywords
Cite
@article{arxiv.2003.08755,
title = {Adaptive binarization based on fuzzy integrals},
author = {Francesco Bardozzo and Borja De La Osa and Lubomira Horanska and Javier Fumanal-Idocin and Mattia delli Priscoli and Luigi Troiano and Roberto Tagliaferri and Javier Fernandez and Humberto Bustince},
journal= {arXiv preprint arXiv:2003.08755},
year = {2020}
}
Comments
11 pages, 3 figures, 3 algorithms, Journal paper under a revision of IEEE Transactions on Image Processing