"Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information."
The study of how computers can interpret visual information, including image recognition and object detection.
Image processing: The study of techniques used to manipulate and analyze digital images.
Image filtering: A process of altering images by changing their digital values or reducing noise.
Segmentation: A process of dividing an image into multiple segments meaning the pixels that belong to a similar category.
Object recognition: The process of identifying and classifying objects within an image or video based on their features and characteristics.
Feature extraction: The process of identifying and capturing important points or attributes of an image that can be used to represent it in a more compact form.
Machine learning: The process of teaching computers to learn from data using various algorithms and statistical models.
Deep learning: A subset of machine learning that uses neural networks with several hidden layers to learn complex representations of data.
Convolutional neural networks: A type of deep neural network commonly used in image recognition tasks. They use filters to scan and identify features within images.
Object detection: The process of locating objects within an image or video frame and determining their presence and position.
Face recognition: The process of identifying human faces within images or video frames.
Optical character recognition: The process of identifying and classifying characters within digital images of text.
Feature matching: The process of identifying similar or corresponding features within different images.
"Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action."
"This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory."
"The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images."
"The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, 3D point clouds from LiDaR sensors, or medical scanning devices."
"The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems."
"Sub-domains of computer vision include scene reconstruction, object detection, event detection, activity recognition, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration."
"Adopting computer vision technology might be painstaking for organizations as there is no single point solution for it."
"There are very few companies that provide a unified and distributed platform or an Operating System where computer vision applications can be easily deployed and managed." Note: The remaining questions can be derived by substituting the relevant terms into the same format used for the first nine questions.