Adaptive Thresholding Algorithm for Grayscale Image Binarization Using MATLAB

This documentation discusses the MATLAB implementation of adaptive thresholding algorithms for grayscale image binarization. These algorithms automatically determine optimal thresholds based on image characteristics and pixel values, effectively converting grayscale images into binary images. The implementation utilizes MATLAB's image processing toolbox functions such as graythresh() for Otsu's method and adaptthresh() for local adaptive thresholding, which calculates thresholds by analyzing local neighborhood statistics around each pixel. These algorithms are particularly valuable in image processing and computer vision applications, enabling efficient image segmentation, edge detection, and object recognition operations. Understanding and mastering these thresholding techniques is essential for professionals working in image processing, as they provide robust solutions for handling varying illumination conditions and complex image backgrounds through intelligent threshold calculation methods.