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World of Computer Vision

A Comprehensive Glossary Explaining Key Terms and Concepts

Vision AI

Vision AI, or computer vision, is a branch of artificial intelligence focused on enabling machines to process, interpret, and understand visual information. It utilizes advanced algorithms, machine learning, and deep learning techniques to mimic human visual processing, empowering machines to recognize objects, classify scenes, and detect patterns for complex visual tasks.

Image recognition

Image recognition is a subset of computer vision that involves identifying objects and patterns in digital images.

Object detection

Object detection is a computer vision technique that involves identifying and localizing objects within an image or video.

Facial recognition

Facial recognition is a computer vision technique that involves identifying and verifying the identity of an individual based on their facial features.

Image segmentation

Image segmentation is a computer vision technique that involves dividing an image into multiple segments or regions, each of which can be analyzed separately.

Deep learning

Deep learning is a subset of machine learning that involves training neural networks with large amounts of data to make predictions or decisions.

Convolutional neural networks (CNN’s)

CNNs are a type of neural network that are particularly well-suited for image and video recognition tasks.

Natural Language Processing (NLP)

NLP is a field of AI that involves training computers to understand and interpret human language.

Semantic search

Semantic search is a search technique that uses natural language processing and machine learning to understand the meaning behind a search query and provide more relevant results.

Structured data

Structured data refers to data that is organized in a structured format, such as a database or spreadsheet, which makes it easier for search engines to understand and interpret.

Machine learning

Machine learning is a field of AI that involves training computers to learn from data and make predictions or decisions without being explicitly programmed.

Transfer learning

Transfer learning is a technique used in machine learning and deep learning where a model is trained on one task and then applied to a different but related task.

Generative adversarial networks (GANs)

GANs are a type of deep learning architecture that can generate new images or videos by learning to mimic real-world examples.


Autoencoders are a type of neural network used for unsupervised learning that can learn to compress and decompress data.

Recurrent neural networks (RNNs)

RNNs are a type of neural network that can process sequential data such as text, audio, or video.

Long short-term memory (LSTM)

LSTM is a type of RNN that can selectively remember or forget certain information over time.

Optical character recognition (OCR)

OCR is a computer vision technique that involves recognizing printed or handwritten text in digital images.

Augmented reality (AR)

AR is a technology that superimposes digital information onto the real world through a camera or other display device.

Virtual reality (VR)

VR is a technology that immerses users in a simulated environment through a headset or other device.

3D modelling

3D modelling is the process of creating digital models of physical objects or environments.

Motion capture

Motion capture is a technique used to record the movements of people or objects for use in animation or other applications.

Edge computing

Edge computing is a computing paradigm that involves processing data locally on devices or at the edge of a network, rather than in a central location.

Internet-of-Things (IoT)

IoT is a network of interconnected devices that can collect and exchange data.

Industry 4.0

Industry 4.0 is the next phase in the digitization of the manufacturing sector, driven by disruptive trends including the rise of data and connectivity, analytics, human-machine interaction, and improvements in robotics.