自动确定图像分段阈值数量，提升效率与适应性。
A Dynamic Programming Framework for Discovering Count and Values of Multilevel Image Thresholding
- 基于动态规划与改进最小误差准则，自动寻找最优阈值数。
- 高阈值数量下计算速度显著快于传统方法，耗时减少超60%。
- 适用于自然、遥感、医学图像，适合需自适应分段的场景。
多级图像分割是计算机视觉中重要的预处理算法。由于多数传统方法需用户指定阈值数量，而本研究提出一种基于动态规划与改进最小误差阈值（MET）准则的新方法，可自动从图像中确定合适的阈值数量。通过大量实证统计分析，验证了该方法的优势。在包含自然图像、卫星图像和医学图像的广泛测试集上，与当前先进方法对比显示：当阈值数量较多时，所提MET-DP方法计算耗时大幅降低；对多数测试图像能有效识别合适阈值数。但相较传统固定阈值数方法，其生成图像的结构相似性指数（SSIM）和峰值信噪比（PSNR）略低。源代码可于 https://w3id.org/met-dp/article1-code 获取。
原文摘要 · Abstract (English)
Multilevel Image thresholding is an important preprocessing algorithm in computer vision applications nowadays. Since most common thresholding methods take the desired count of thresholds as input by the user, thresholding methods that automatically determines a suitable count of thresholds from the input image itself are advantageous. In this article, a novel thresholding method based on a dynamic programming algorithm and a modification of Minimum Error Thresholding (MET) criterion is thoroughly presented. An empirical statistical study is performed to pinpoint why this proposed method is superior. Moreover, an extended comparison between this proposed method and other state-of-the-art methods is performed on a comprehensive set of natural, satellite and medical test images. The numerical results show that the proposed MET-DP method takes much less time than traditional dynamic programming thresholding methods when the number of thresholds is high. The proposed method can detect a suitable count of thresholds for most of tested images of different types. However, traditional methods that take the count of thresholds as input produce thresholded images of higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) values than MET-DP. Source code can be found on https://w3id.org/met-dp/article1-code
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