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A Generalized and Parallelized SSIM-Based Multilevel Thresholding Algorithm
Applied Artificial Intelligence ( IF 2.8 ) Pub Date : 2019-11-04 , DOI: 10.1080/08839514.2019.1683986
Ikram Boubechal 1 , Rachid Seghir 1 , Redha Benzid 2
Affiliation  

ABSTRACT Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to a certain objective function. In this work, we generalize the use of the SSIM quality measure as an objective function to solve the multilevel thresholding problem using empirically tuned swarm intelligence algorithms. The experimental study we have conducted shows that our approach, producing near-exact solutions, is more effective compared to the state-of-the-art methods. Moreover, we show that the computation complexity has been significantly reduced by adopting a shared-memory parallel programming paradigm for all the algorithms we have implemented.

中文翻译:

一种基于SSIM的通用并行化多级阈值算法

摘要 多级阈值是一种广泛使用的图像分割技术。它包括通过根据某个目标函数找到最佳阈值,将输入图像分成几个不同的区域。在这项工作中,我们概括使用 SSIM 质量度量作为目标函数,以使用经验调整的群智能算法解决多级阈值问题。我们进行的实验研究表明,与最先进的方法相比,我们的方法产生近乎精确的解决方案更有效。此外,我们表明,通过为我们实现的所有算法采用共享内存并行编程范式,计算复杂度已显着降低。
更新日期:2019-11-04
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