"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 use of machines to understand and interpret visual data, including image recognition and object detection.
Image processing: This involves techniques used to improve the quality of digital images, correct image distortion or remove noise.
Feature extraction: This refers to the process of extracting specific features or patterns from an image or a dataset.
Object recognition: This involves the identification and localization of objects within an image, typically using machine learning algorithms.
Image segmentation: This is the process of dividing an image into multiple segments or regions, each representing a different object or region of the image.
Deep learning: This is a subset of machine learning that involves the use of neural networks to create more advanced models for image analysis.
Convolutional neural networks (CNNs): These are deep learning models that have been designed to work specifically with images or visual data.
Supervised learning: This is a type of machine learning in which the algorithm is trained using labeled data, with the goal of predicting future outcomes.
Unsupervised learning: This is a type of machine learning in which the algorithm is trained using unlabeled data, with the goal of discovering patterns or relationships within the data.
Transfer learning: This is a technique in which a pre-trained model is used in a new context, often to improve the accuracy of a new model or to reduce training time.
Data augmentation: This is the process of artificially increasing the size of a dataset by creating variations of existing data, such as by flipping or rotating images.
Computer vision applications: These include areas such as object detection, image and video recognition, autonomous driving, surveillance, and medical image analysis.
Optimization techniques: These include methods for improving the performance of machine learning models, such as gradient descent, backpropagation, and regularization.
Metrics and evaluation: These are the tools used to assess the accuracy and performance of machine learning algorithms, such as precision, recall, and F1 score.
Probability and statistics: These provide a foundation for understanding the underlying principles of machine learning and computer vision.
Programming languages and libraries: These include languages such as Python and programming libraries such as OpenCV and TensorFlow, which are commonly used in computer vision and AI.
Object detection: Identifying and locating objects within an image or video frame.
Image segmentation: Separating an image into multiple regions, allowing for more accurate identification and analysis of objects.
Image classification: Assigning a label to an image based on its contents.
Facial recognition: Identifying and verifying the identity of a person based on their facial features.
Object tracking: Following a particular object or group of objects as they move through a video sequence.
Optical character recognition (OCR): Identifying and extracting text from images or video frames.
Gesture recognition: Recognizing and interpreting hand or body gestures for use in human-computer interaction.
Scene reconstruction: Using multiple images or video frames to create 3D models of scenes and objects.
Human pose estimation: Inferring the 3D position and orientation of the human body from 2D images or video frames.
Video analytics: Analyzing large amounts of video data for detecting events or anomalies.
Anomaly detection: Identifying unusual or anomalous patterns in data/images.
Semantic segmentation: Identifying objects in images and categorizing them into pre-defined categories.
"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.