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Thresholding Techniques in Computer Vision Applications

Riyaz Mohammed M.*, Sabibullah M.**

*-** Department of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India.

Periodicity:April - June'2024
DOI : https://doi.org/10.26634/jip.11.2.21001

Abstract

Thresholding techniques are key pillars of image processing, especially for distinguishing objects in complex environments. This paper examines four types of thresholding strategies, each based on different theories, practical, popular, and advanced. Through a thorough literature review, the paper explains the thresholding techniques, thresholding operations, evaluation metrics, image processing techniques, and Python code for ROI of binary images in an understandable manner. The findings underscore the significance of thresholding in various applications, from object recognition to medical imaging, and highlight the importance of selecting appropriate thresholding methods based on image characteristics.

Keywords

Thresholding, Computer Vision, Image Segmentation, Binary Image, Threshold Selection, Python Code on ROI.

How to Cite this Article?

Mohammed, M. R., and Sabibullah, M. (2024). Thresholding Techniques in Computer Vision Applications. i-manager’s Journal on Image Processing, 11(2), 27-34. https://doi.org/10.26634/jip.11.2.21001

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