Optical Flow

Computer Vision

The field of AI that enables computers to interpret and understand visual information from images and video.

Image Classification

The task of assigning a label or category to an entire image based on its visual content.

Object Detection

Identifying and locating specific objects within an image by drawing bounding boxes around them.

Optical Character Recognition

Technology that converts images of text into machine-readable text data.

Facial Recognition

Technology that identifies or verifies individuals by analysing facial features and patterns in images or video.

Depth Estimation

Predicting the distance of surfaces in a scene from the camera viewpoint using visual information.

Super Resolution

Enhancing the resolution and quality of images beyond their original pixel count using AI techniques.

Video Understanding

Analysing and interpreting the content, actions, and events within video sequences using computer vision.

Action Recognition

Identifying and classifying human actions or activities from video sequences.

Visual Question Answering

An AI task that generates natural language answers to questions about the content of images.

Image Captioning

Automatically generating natural language descriptions of the content depicted in images.

YOLO

You Only Look Once — a real-time object detection algorithm that processes entire images in a single neural network pass.

Creating new images from scratch using generative AI models like GANs, diffusion models, or VAEs.

Partitioning an image into multiple segments or regions, assigning each pixel to a specific class or object.

The process of aligning two or more images of the same scene taken at different times, viewpoints, or by different sensors.

A set of data points in 3D space, typically generated by LiDAR or depth sensors, representing surface geometry.

Classifying every pixel in an image into a predefined category without distinguishing between individual object instances.

A rectangular region drawn around an object in an image to indicate its location for object detection tasks.

Applying the visual style of one image to the content of another image using neural networks.

Applying transformations like rotation, flipping, and colour adjustment to training images to improve model robustness.