What is Thresholding in Image Processing? A Guide.

Thresholding is a pivotal technique in image processing used to convert grayscale images into binary images by setting a pixel intensity cutoff, aiding in applications such as image segmentation, object detection, and feature extraction. This technique can be global, using a single threshold for the entire image, or local, adjusting thresholds based on local regions to accommodate varying lighting conditions. Global methods, like Otsu's, are effective with bimodal histograms, while local methods, including adaptive mean and Gaussian thresholding, Niblack's, Sauvola's, and Bernsen's methods, adapt to non-uniform illumination by considering local image properties. Implementations of these methods in OpenCV, Scikit-Image, and Mahotas showcase practical thresholding applications, enhancing tasks such as edge detection, document binarization, and medical imaging.