Bisection Method-Based Adaptive Thresholding for Robust Edge Detection in Image Processing
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Abstract
Thresholding is the process of dividing an image into a collection of tiny numbers known as pixels, which correspond to a physical volume in digital image processing. Image thresholding can enhance image quality by reducing noise and perfecting overall visual clarity. In this environment, different ways are used for image processing, including, Sobel, Prewitt, Kirsch, and canny drivers. But all of them have several limitations which we will try to overcome using bisection system of numerical styles. According to the numerical style theory known as the "bisection system," if a function f(x) is nonstop between a and b, and f(a) and f(b) are of contrary signs, also there exists at least one root bittern a and b. In this paper we will bandy the former styles of thresholding for edge discovery and bisection system for edge discovery in image processing.