Efficient and Improved Edge Detection Via a Hysteresis Thresholding Method

Khan, Sajid Ali; Lee, Dong‐Ho; Khan, Muhammad Asif; Gilal, Abdul Rehman; Iqbal, Junaid; Waqas, Ahmad

doi:10.18520/cs/v118/i6/954-960

Cited by 6 publications

(1 citation statement)

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“…To create a final edge map, hysteresis thresholding calculates both high and low thresholds. If the low and high thresholds are appropriately calculated and their thresholded images are processed, it produces much better results than single thresholding [22].…”

Section: Hysteresis Methodsmentioning

confidence: 99%

Optimization Methods for Image Thresholding: A review

Abd,

Hussein Alrawi,

Bassel

2023

JUAPS

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Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated.

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Section: Hysteresis Methodsmentioning

confidence: 99%