Morphological Operations on Images

Morphological operations are transformations that are applied to images (specifically on binary images). These operations process the images based on their shapes It uses Structuring Element for this purpose it is a kernel mask. Morphological operations are a crucial tool in the field of image processing. But, you might be wondering, why do we even use these techniques? The answer is simple: morphological operations allow us to manipulate images in powerful ways, enhancing certain features and removing noise, among other things.

It shrinks the shape of the image by removing pixels from the boundaries. It works on binary images (having 0 and 1) if the area of the image which comes under the kernel mask is all 1 then we consider 1 otherwise 0.

Dilation expands the shape of the image by adding pixels to the boundaries of the shape. It also uses a structuring element, if the area of the image which comes under the kernel mask has at least 1 pixel having 1 then we considered 1 otherwise 0.

Let's dive into coding, we are going to use OpenCV to perform these operations on images.

First, we will import the important libraries .

In the above picture, I also made a helping function for visualizing the image.

In the second step, we will read the RGB image and convert it into a gray-scale image then apply binarization to that image.

But what is the Binarization of an image?

It converts the gray-scale image into black and white, so we may call it a binary image.

In image processing or computer vision, learning about morphological operations is a must. Not only can these techniques help you improve the quality of your images, but they also have numerous practical applications, from medical diagnosis to surveillance to autonomous driving.

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