Neighborhood operations in image processing in computer vision
This post is about brief understanding of neighborhood operations in image preprocessing these are hidden workers of common image processing operations such as smoothing and edge detection, in spatial domain of image. Post begins with definition, explanation of working numerically , give simple python Implementation example , it’s various applications and some limitations in the last part of the post. So, let’s start the journey of exploration of neighborhood operations in image preprocessing.
What is neighborhood operations?
Neighborhood operations in image preprocessing involves manipulating the values of a given pixel based on the values of its neighboring pixels. These operations typically use a local region, or pixels surrounding in the pixel to compute its new value.
In above image matrix value of pixel at x location updated based on the pixels around it which having values — 120,201,100,50,45,10,20,56 based on the requirement functions applied using these pixel values, for example if want to blurring region with mean filter than average of around pixels value is new value of location x.