多级图像阈值处理是用于图像分割的公知技术。最近,已经提出了各种元启发式方法来确定多级图像分割的阈值。这些方法主要基于隐喻,它们具有很高的复杂性,并且它们的收敛速度相对较慢。在本文中,提出了一种多级图像阈值化方法,该方法通过使用简单的优化技术而不是基于隐喻的算法来简化阈值化问题。更具体地说,在本文中,开发了混沌增强Rao(CER)算法,其中八个混沌映射,即Logistic,Sine,正弦,高斯,Circle,切比雪夫,歌手,使用帐篷。此外,在开发的CER算法中,阈值的数量是自动确定的,而不是手动确定。所开发的CER算法的性能基于不同的统计分析指标进行评估,即BDE,PRI,VOI,GCE,SSIM,FSIM,RMSE,PSNR,NK,AD,SC,MD,和NAE。实验工作和相关评价是在BSDS300数据集上进行的。实验结果表明,所提出的CER算法优于基于PRI的比较方法,SSIM,FSIM,PSNR,RMSE,AD,和NAE指标。此外,该方法在速度和精度方面具有更好的收敛性。
Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced Rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy.