论文标题
基于模糊积分的自适应二进制
Adaptive binarization based on fuzzy integrals
论文作者
论文摘要
自适应二进制方法学阈值与利用积分图像相邻像素相对于相邻的像素的强度。反过来，通常使用汇总台式算法（SAT）最佳地计算积分图像。本文档通过有效设计用于模糊积分的修改后的SAT，基于模糊积分图像提供了一种新的自适应二进制技术。我们将这种新方法定义为平坦（模糊的本地自适应阈值）。实验结果表明，所提出的方法的产生的图像质量阈值通常比传统算法和显着性神经网络更好。我们提出了Sugeno和CF 1,2积分的新概括，以通过有效的积分图像计算来改善现有结果。因此，这些新的广义模糊积分可以用作实时和深度学习应用程序灰度处理的工具。索引术语：图像阈值，图像处理，模糊积分，聚合功能
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
