Image representation in Computer Vision

This post is about brief understanding of different techniques used for image representation in computer vision.

Introduction

Image representation in computer vision refers to the process of converting an image into a numerical or symbolic form that can be easily understood and processed by a computer. Images are typically represented as a collection of pixels, where each pixel corresponds to a specific color or intensity value. The goal of image representation is to extract relevant features and information from the image, enabling the computer to perform various tasks, such as object recognition, image classification, and image segmentation.

Techniques

There are several common techniques for image representation in computer vision:

Grayscale representation: Images are represented using a single channel where each pixel contains a grayscale value ranging from 0 (black) to 255 (white). This representation is commonly used for tasks that do not require color information.

Color representation: For color images, the most common representation is the RGB (Red, Green, Blue) format. Each pixel is represented by three color channels (R, G, and B), with each channel containing an intensity value ranging from 0 to 255.