  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
