Why do We Need Binarization?
Auto encoders are not able to recognize the images because of the noise in the images, otherwise referred to as “image processing.” For avoiding the background noise generated in images we will use a Binarization technique commonly empoloyed with artificial intelligence.
A Breakdown of Binarization
A color image consists of 3 channels (Red, Green and Blue) with values ranging from 0 to 255. One of the key features of binarization is converting grey scale images into black and white (0 and 1). What’s more, binarization provides sharper and clearer contours of various objects present in the image. This feature extraction improves the learning of AI models.
In the process of image binarization a threshold value is chosen, and all pixels with values above this threshold are classified as white, and all other pixels as black. The problem then is how to select the correct threshold (otherwise referred to as a thresholding method).
One can see that binarization takes an image with foreground/background and returns the binary image. It discards the background noise and gives the contour of the image in the foreground.
Steps involved in Image Binarization
The ‘imager ‘ package uses the K-means method to automatically identify the threshold for an image and this method is equivalent to globally optimal version of popular Otsu’s method. It is very important to know that an incorrect threshold value can result in distorted binary images, where parts of the object could be missing.
Manual Threshold Identification
Given an image, how do we calculate the threshold value?
The image below illustrates a histogram-based method which uses pixel values.