In this chapter, we have reviewed the main thresholding techniques you can use to threshold your images. Thresholding techniques can be used for many computer vision tasks (for example, text recognition and image segmentation, among others). Both simple and adaptive thresholding techniques have been reviewed. Additionally, we have seen how to apply Otsu's binarization algorithm and the triangle algorithm to automatically select a global threshold for thresholding your images. Finally, we have seen how to use different thresholding techniques using scikit-image. In this sense, two global thresholding techniques (Otsu's and triangle algorithms) and two local thresholding techniques (Niblack's and Sauvola's algorithms) have been applied to a test image.
In Chapter 8, Contour Detection, Filtering, and Drawing, we will see how to deal with contours, which...
