融合视觉模型和最大熵的阈值分割算法

针对传统二维最大熵阈值分割算法关于二维直方图的区域划分中存在的缺点(即图像的部分目标点和背景点错误划分为边缘点或噪声点，而把部分边缘点和噪声点划分为目标点和背景点)以及搜索最佳阈值向量的时间复杂度较高的缺点，提出了采用视觉模型构造二维直方图，并提出了一种二维直方图的新的区域划分方法，同时还提出了基于视觉模型的二维最大熵阈值分割算法，提出的阈值分割算法降低了计算复杂度的同时还具有很好的分割性能。根据一些图像分割的定量评价标准，做了一系列实验，与几种典型的二维阈值分割算法相比，提出算法的分割效果更好。

Image Thresholding Segmentation Based on Human Vision Model and Maximum Entropy

The traditional two-dimensional image thresholding segmentation algorithms exist some shortcomings that are the area division of two-dimensional histogram (Part of the target points and background points is divided into edge points or noise points, while part of the edge points and noise points is divided into the target point and background points) and high time complexity of searching the best threshold vector, a new two-dimensional histogram is proposed by using human vision model, and a new region division method about two-dimensional histogram is proposed, and the same time image thresholding segmentation based on human vision model and maximum entropy is proposed, the proposed image thresholding segmentation algorithm reduces the time complexity and has good segmentation performance. According to some evaluation standards for image segmentation result, a series of experiments show the proposed algorithm has better segmentation effect compared with several typical two-dimensional threshold segmentation algorithms.