Computer Vision Applications
Expert-defined terms from the Professional Certificate in Artificial Intelligence for Quality Management Pioneers course at London School of Planning and Management. Free to read, free to share, paired with a globally recognised certification pathway.
Algorithm #
A set of rules or procedures for solving a problem or accomplishing a task, especially by a computer. In Computer Vision, algorithms are used to analyze and interpret images and video.
Artificial Intelligence (AI) #
The simulation of human intelligence in machines that are programmed to think and learn. AI is used in Computer Vision to enable machines to interpret and understand visual data.
Computer Vision #
The field of study focused on enabling computers to interpret and understand visual data from the world, in the form of images and videos.
Convolutional Neural Network (CNN) #
A type of deep learning model that is commonly used in Computer Vision. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from images.
Deep Learning #
A subset of machine learning that is based on artificial neural networks with representation learning. Deep learning models are able to learn and improve from experience and data.
Feature Extraction #
The process of extracting useful features or characteristics from images or videos for use in Computer Vision tasks.
Image Classification #
The process of categorizing images into one of several classes or labels based on their visual content.
Image Recognition #
The ability of a machine or computer to identify and categorize images based on their visual content.
Instance Segmentation #
The process of identifying and segmenting individual objects within an image, while also classifying each object.
Object Detection #
The process of identifying and locating objects within an image or video.
Optical Character Recognition (OCR) #
The process of converting different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.
Pascal Visual Object Classes (VOC) #
A standardized dataset used for object detection and image classification tasks in Computer Vision.
Region #
based Convolutional Neural Network (R-CNN): A type of deep learning model used for object detection in Computer Vision. R-CNNs use a region proposal algorithm to identify potential objects in an image, and then classify and refine those proposals using a CNN.
Semantic Segmentation #
The process of partitioning an image into multiple segments, with each segment being assigned a class label.
Support Vector Machine (SVM) #
A type of supervised learning algorithm that can be used for classification or regression tasks. SVMs are often used in Computer Vision for image classification and object detection.
Transfer Learning #
The process of applying a pre-trained deep learning model to a new, related task. Transfer learning can save time and resources by allowing a model to leverage the knowledge it has already gained from a large dataset.
Unsupervised Learning #
A type of machine learning that does not require labeled data. Unsupervised learning algorithms find hidden patterns or intrinsic structures from input data.
YOLO (You Only Look Once) #
A real-time object detection system that is able to detect objects in images and videos with high accuracy and speed. YOLO treats object detection as a regression problem, and is able to make predictions in a single pass through the network.
Z #
score Normalization: A method of normalizing data by transforming values to have a mean of 0 and a standard deviation of 1. Z-score normalization is often used in Computer Vision to prepare data for use in deep learning models.
In summary, Computer Vision Applications in the context of Professional Certific… #
Understanding these terms and concepts is essential for anyone looking to work in the field of Computer Vision and Artificial Intelligence.