Computer Vision is a field of artificial intelligence (AI) and computer science that focuses on enabling machines to interpret, analyse, and understand visual information from the world, such as images and videos. The ultimate goal of computer vision is to give computers the ability to “see” and make decisions or perform tasks based on visual input, similar to human vision but with far greater speed and accuracy in specific tasks.
Key tasks in computer vision include:
- Image Recognition: Identifying objects, people, scenes, or activities within an image. For example, identifying a cat in a photo or recognising faces.
- Object Detection: Locating and identifying objects within an image or video. This is useful for applications such as autonomous vehicles, where the car needs to detect pedestrians, other cars, and road signs.
- Image Classification: Categorising an entire image into a specific class or label. For example, determining whether an image contains a dog, cat, or neither.
- Semantic Segmentation: Dividing an image into regions or segments that correspond to different objects, giving each pixel a label that identifies the object it belongs to. This technique is useful in medical imaging and robotics.
- Image Generation: Generating new images from input data, which includes techniques like image synthesis, style transfer, or creating entirely new images from text prompts (as seen in AI art generation).
- Face Recognition: Detecting and identifying human faces in images or videos. This has applications in security systems, social media tagging, and more.
- Motion Detection and Tracking: Identifying and following the movement of objects in a video sequence, such as tracking a person’s motion in a surveillance camera feed or a ball in a sports match.
- 3D Reconstruction: Building a 3D model from multiple 2D images or videos, often used in augmented reality (AR) and virtual reality (VR) applications.
Applications are found across a wide range of industries, including:
- Autonomous Vehicles: Enabling self-driving cars to interpret road conditions, recognise traffic signs, and detect pedestrians.
- Healthcare: Assisting in the diagnosis of medical images like X-rays, MRIs, or CT scans to detect diseases.
- Retail: Supporting automated checkout systems and improving inventory management through image-based recognition.
- Manufacturing: Performing quality control by detecting defects in products on an assembly line.
- Security and Surveillance: Facial recognition and video monitoring systems to enhance security.
Computer vision systems typically rely on machine learning and deep learning techniques, particularly using convolutional neural networks (CNNs), which are highly effective for analysing visual data and patterns.