IEEE Trans Image Process. 2013 Dec;22(12):4788-97. doi: 10.1109/TIP.2013.2277832. Epub 2013 Aug 15.
Multilevel thresholding amounts to segmenting a gray-level image into several distinct regions. This paper presents a 2D histogram based multilevel thresholding approach to improve the separation between objects. Recent studies indicate that the results obtained with 2D histogram oriented approaches are superior to those obtained with 1D histogram based techniques in the context of bi-level thresholding. Here, a method to incorporate 2D histogram related information for generalized multilevel thresholding is proposed using the maximum Tsallis entropy. Differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computational efficiency of the proposed method. The performance of DE is investigated extensively through comparison with other well-known nature inspired global optimization techniques such as genetic algorithm, particle swarm optimization, artificial bee colony, and simulated annealing. In addition, the outcome of the proposed method is evaluated using a well known benchmark--the Berkley segmentation data set (BSDS300) with 300 distinct images.
多阈值处理相当于将灰度图像分割成几个不同的区域。本文提出了一种基于二维直方图的多阈值处理方法，以提高目标之间的分离度。最近的研究表明，在双阈值处理的情况下，二维直方图定向方法的结果优于基于一维直方图的技术的结果。这里，提出了一种使用最大塔利斯熵将二维直方图相关信息纳入广义多阈值处理的方法。差分进化（DE）是一种简单而有效的当前感兴趣的进化算法，被用于提高所提出方法的计算效率。通过与其他著名的基于自然的全局优化技术（如遗传算法、粒子群优化、人工蜂群和模拟退火）的比较，广泛研究了 DE 的性能。此外，还使用一个著名的基准——伯克利分割数据集（BSDS300）中的 300 个不同图像来评估所提出方法的结果。
