In c++

What is image binarization? In computer vision, image binarization, a.k.a. thresholding is the process of taking a grayscale image and converting it into a black and white image. In grayscale images, every pixel represents an intensity value ranging from o (black) to 255 (white). In black and white images, every pixel is either o or 255. Intensity refers to the brightness of a color, white is the brightest and therefore the most intense, black is the darkest and the least intense. The figure below shows an example of image binarization: Global Thresholding It's important to note that pixel values for an image are stored in a matrix. Matrices don't have to be perfect squares (i.e., 2X2, 3X3, 10x10), and while all most of our examples below are perfect squares, your program should work on images of any dimensions (perfect squares or not). The algorithm below shows how to binarize an image. input: Image A (grayscale) output: Image B (black and white) calculate global T for each pixel A[i][j] in A do if A[i][j] either 'local' or 'global' name of the input file name of the output file [] size of the neighborhood What is image binarization? In computer vision, image binarization, a.k.a. thresholding is the process of taking a grayscale image and converting it into a black and white image. In grayscale images, every pixel represents an intensity value ranging from o (black) to 255 (white). In black and white images, every pixel is either o or 255. Intensity refers to the brightness of a color, white is the brightest and therefore the most intense, black is the darkest and the least intense. The figure below shows an example of image binarization: Global Thresholding It's important to note that pixel values for an image are stored in a matrix. Matrices don't have to be perfect squares (i.e., 2X2, 3X3, 10x10), and while all most of our examples below are perfect squares, your program should work on images of any dimensions (perfect squares or not). The algorithm below shows how to binarize an image. input: Image A (grayscale) output: Image B (black and white) calculate global T for each pixel A[i][j] in A do if A[i][j] either 'local' or 'global' name of the input file name of the output file [] size of the neighborhood