Introduction to Computer Vision

What Is Computer Vision?

Computer Vision is a branch of AI that enables computers to extract information from images and videos.

An image can be represented as a grid of pixels. In a color image, each pixel typically contains red, green, and blue channel values.

Computer Vision models learn patterns in these pixel values to identify objects, classify scenes, detect abnormalities, and interpret visual content.

Common Computer Vision Tasks

Image Classification: Assigns a label to an entire image, such as identifying whether an image contains a cat or a dog.

Object Detection: Identifies objects and their approximate locations, usually using bounding boxes.

Image Segmentation: Assigns labels to individual pixels or regions, allowing more detailed scene understanding.

Face Detection: Identifies the presence and location of faces in an image.

Optical Character Recognition (OCR): Extracts text from images, scanned documents, and photographs.

How Computer Vision Models Work

A typical image-processing workflow includes:

1. Collecting and labeling image data when labels are required.
2. Resizing images and normalizing pixel values.
3. Passing images through a trained model.
4. Interpreting predicted classes, boxes, or masks.
5. Evaluating performance on suitable test data.

Convolutional Neural Networks (CNNs) have historically been important for image analysis. Modern systems also use vision transformers and other architectures.