对比四类直方图图像分割方法，帮你看清优劣与适用场景。
A Comparative Tutorial of the Histogram-based Image Segmentation Methods
- 按均值、高斯混合、熵、特征点四类分类直方图分割法
- 系统评估经典方法在准确性与鲁棒性上的差异
- 对比深度学习方法，适合初学者与工程选型参考
图像直方图是像素灰度分布的精确图形表示，也是像素概率分布的估计。因此，直方图被广泛用于计算聚类中心和分割阈值。已有众多经典直方图图像分割方法在学术界和工业界发挥重要作用。本文首先回顾直方图分割技术的历史与最新进展，将其分为四类：(1) 均值法，(2) 高斯混合模型法，(3) 熵基法，(4) 特征点法。本教程旨在三方面：1）向读者传授经典直方图分割方法的原理；2）客观评估各类方法的优缺点；3）客观比较经典方法与当前先进深度学习方法的性能表现。
原文摘要 · Abstract (English)
The histogram of an image is the accurate graphical representation of the numerical grayscale distribution and it is also an estimate of the probability distribution of image pixels. Therefore, histogram has been widely adopted to calculate the clustering means and partitioning thresholds for image segmentation. There have been many classical histogram-based image segmentation methods proposed and played important roles in both academics and industry. In this tutorial, the histories and recent advances of the histogram-based image segmentation techniques are first reviewed and then they are divided into four categories: (1) the means-based method, (2) the Gaussian-mixture-model-based method, (3) the entropy-based method and (4) the feature-points-based method. The purpose of this tutorial is threefold: 1) to teach the principles of the classical histogram-based image segmentation methods to the interested readers; 2) to evaluate the advantages and disadvantages of these classical histogram-based image segmentation methods objectively; 3) to compare the performances of these classical histogram-based image segmentation methods with state-of-the-art deep learning based methods objectively.
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