Multilevel thresholding using grey wolf optimizer for image segmentation
作者：
Highlights：
• A new method of multilevel thresholding for image segmentation using Grey Wolf Optimizer (GWO) is proposed.
• Two objective functions-Kapur's entropy and Otsu's between class variance are used.
• The proposed method is more stable than PSO and BFO based methods.
• Yields solutions of higher quality than PSO and BFO based methods.
• Faster than BFO but slower than PSO.
摘要
•A new method of multilevel thresholding for image segmentation using Grey Wolf Optimizer (GWO) is proposed.•Two objective functions-Kapur's entropy and Otsu's between class variance are used.•The proposed method is more stable than PSO and BFO based methods.•Yields solutions of higher quality than PSO and BFO based methods.•Faster than BFO but slower than PSO.
论文关键词：Multilevel thresholding,Image segmentation,Grey wolf optimizer,Kapur's entropy,Otsu's threshold
论文评审过程：Received 14 March 2016, Revised 14 April 2017, Accepted 15 April 2017, Available online 17 April 2017, Version of Record 26 May 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.029
