Multi-level image thresholding using Otsu and chaotic bat algorithm

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their...

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Vydáno v:	Neural computing & applications Ročník 29; číslo 12; s. 1285 - 1307
Hlavní autoři:	Satapathy, Suresh Chandra, Sri Madhava Raja, N., Rajinikanth, V., Ashour, Amira S., Dey, Nilanjan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.06.2018 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Automatic target recognition Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Heuristic methods Image processing Image Processing and Computer Vision Image segmentation Object recognition Original Article Particle swarm optimization Probability and Statistics in Computer Science Signal to noise ratio Target recognition Thresholds Multi-level thresholding Bat algorithm Structural similarity index (SSIM) Otsu method Ikeda Map Peak signal to noise ratio (PSNR)
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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