To overcome the shortcomings of 1-D and 2-D Otsu’s thresholding methods, a 3-D Otsu method has been introduced. While yielding satisfactory segmentation results for images with a low signal-to-noise ratio (SNR) and poor contrast, it has the downside of high computational complexity. In this paper, the cuttlefish algorithm (CFA)-based 3-D Otsu thresholding method is proposed to pace up the conventional 3-D Otsu thresholding for color image segmentation. In order to decrease the effects of noises and weak edges, an optimally selected multilevel 3-D Otsu image thresholding technique is brought into the proposed segmentation scheme. The CFA is a newly developed stochastic meta-heuristic optimization algorithm based on observing, mimicking, and modeling the camouflaging feature of cuttlefish. It is used to simplify the problem of exhaustive search for the optimal threshold vector in 3-D space. Experimental results, when compared to 1-D Otsu, 1-D Otsu-Cuckoo search (CS) algorithm, 1-D Otsu-lightning search algorithm (LSA), 1-D Otsu-CFA, conventional 3-D Otsu, 3-D Otsu-CS, and 3-D Otsu-LSA, indicate that the proposed algorithm CFA-based 3-D Otsu thresholding is superior to all the other multilevel thresholding algorithms. The proposed 3-D-CFA method produces promising segmentation results from the objective and subjective aspects.

中文翻译：

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用于彩色图像分割的基于乌贼算法的多级 3D Otsu 函数

为了克服 1-D 和 2-D Otsu 阈值方法的缺点，引入了 3-D Otsu 方法。虽然对于低信噪比 (SNR) 和对比度差的图像产生令人满意的分割结果，但它具有计算复杂度高的缺点。在本文中，提出了基于乌贼算法 (CFA) 的 3-D Otsu 阈值方法，以加快用于彩色图像分割的传统 3-D Otsu 阈值处理。为了减少噪声和弱边缘的影响，在提出的分割方案中引入了优化选择的多级 3-D Otsu 图像阈值技术。CFA是一种新开发的基于观察、模仿和建模墨鱼伪装特征的随机元启发式优化算法。它用于简化在 3-D 空间中穷举搜索最优阈值向量的问题。实验结果，与 1-D Otsu、1-D Otsu-Cuckoo 搜索 (CS) 算法、1-D Otsu-lightning 搜索算法 (LSA)、1-D Otsu-CFA、传统 3-D Otsu、3- D Otsu-CS 和 3-D Otsu-LSA 表明所提出的基于 CFA 的 3-D Otsu 阈值算法优于所有其他多级阈值算法。所提出的 3-D-CFA 方法从客观和主观方面产生了有希望的分割结果。表明所提出的基于 CFA 的 3-D Otsu 阈值算法优于所有其他多级阈值算法。所提出的 3-D-CFA 方法从客观和主观方面产生了有希望的分割结果。表明所提出的基于 CFA 的 3-D Otsu 阈值算法优于所有其他多级阈值算法。所提出的 3-D-CFA 方法从客观和主观方面产生了有希望的分割结果。