Image Recognition definition: Uses + workings

Image Recognition technology enables computers to identify and understand objects, people‌ or scenes, digitally. It's like teaching a computer to "see" and recognise things the way humans do. For example, when a photo is uploaded on Facebook, it can automatically recognise the faces and suggest you ‌tag your friends.

Image Recognition, a branch of Artificial Intelligence and Computer Vision, has a wide array of applications across various industries👇

Security and authentication

Image Recognition helps security systems by enabling ‘face unlock’, where users can access devices using their facial identification feature. This also aids in ‘ID verification’ by automatically confirming the authenticity of passports, driver's licences or other identification documents.

Healthcare

In healthcare, image recognition helps professionals interpret X-rays and other medical images to diagnose diseases. Additionally, it also helps in monitoring health by tracking changes in a patient's condition through photos or videos, such as in wound healing or skin conditions.

Retail and shopping

For retail, Image Recognition enables ‘visual search’, allowing users to search for products by taking a photo or uploading an image. Additionally, features like ‘inventory management’ automatically tracks and manages product inventory through image scanning.

Autonomous vehicles and transportation

Image recognition helps autonomous vehicles understand their surroundings by processing camera inputs for object detection, traffic signs‌ and other elements on the road.

Quality control in manufacturing

In manufacturing, it helps in detecting defects, inspecting products on assembly lines and ensuring packaging consistency by verifying that product packaging meets standards through image comparison.

Image Recognition works by using advanced algorithms and Machine Learning models to analyse and interpret visual content, enabling computers to understand and respond to images or videos.

Preprocessing

The image or video is first preprocessed to enhance its quality and make it easier for the algorithm to analyse. This step might involve resizing, adjusting brightness or contrast‌ and applying filters to reduce noise.

Feature extraction

The image is then broken down into smaller parts, known as features like edges, corners, textures‌ or other distinctive characteristics to focus on the most relevant parts of the image.

Model training

A Machine Learning model, like Convolutional Neural Network (CNN), is trained on a large dataset of labelled images to associate specific features with certain objects, scenes‌ or concepts. For example, it might learn that a cat with round edges and a certain texture is likely to be a cat.

Classification or object detection

Once trained, the model can analyse new, unseen images. It uses the features it's learned to classify the entire image.

Post-processing

The model's output is then refined to improve accuracy. This step includes techniques like noise reduction to enhance the final output through error correction and visualisation for better usability.

Computer Vision is the field of study that aims to give computers the ability to understand and interpret visual data from the world, much like human vision does. It encompasses a wide range of tasks and techniques, including image processing, feature extraction, pattern recognition‌ and more.

Further, Computer Vision also includes applications like Image Recognition and Object Detection. Image Recognition is a specific task within Computer Vision that focuses on identifying and categorising entire images based on their main content. For example, an image recognition system might be trained to distinguish between pictures of cats and dogs.

Object Detection is another specific task within Computer Vision, but it goes a step further than Image Recognition, which involves identifying and locating multiple objects within an image, providing information about their positions and sizes, typically using bounding boxes.