OpenCV: Adaptive and Otsu Threshold in Image Processing with Python

Image pre-processing techniques in artificial intelligence

Otsu Image Threshold. An image by the Author

What is OpenCV?

This library is a nutshell and robust collection of image processing methods that drive the vision application in machine learning and deep learning.

To install the OpenCV library in anaconda

pip install opencv-python

What is the image threshold?

In computer vision applications i.e. image segmentation is an important aspect in which the object in the image is separated into foreground and background.

To make an effective threshold need a grayscale image of one channel in which each image pixels value lies between 0 to 255. The value going toward zero considers white and the value going toward 255 considers black.

The Otsu method is an automatic threshold value optimizer from the bimodal images. The bimodal images are the pixels value that differentiates the image in the foreground(object) and background.

The example of a bimodal image that contains two distributions in the histogram defines the separation of two objects in the grayscale image.