"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."
Hardware and software systems used for vision-based robots.
Image processing: The study of algorithms and techniques used to analyze and manipulate images captured by cameras, such as filtering, segmentation, feature extraction, etc.
Optics: The science of light and its behavior; includes topics such as lenses, diffraction, reflection, refraction, etc.
Camera technology: The study of the components of a camera, such as sensors, lenses, and image processors.
Pattern recognition: The ability to identify objects, shapes, and patterns in images using machine learning and other techniques.
Machine learning: The study of algorithms that can learn from data and make predictions, such as neural networks, decision trees, etc.
Deep learning: A subset of machine learning that uses neural networks with many layers to perform complex tasks such as object recognition.
Cameras and image sensors: The various types of cameras and sensors used in robotics, including CCD, CMOS, and Infrared sensors.
Robotics hardware: The components used in robotics systems, including actuators, sensors, and processors.
Computer vision: A field of study focused on enabling computers to recognize images and objects, including image and video processing, feature extraction, object recognition and tracking, etc.
Programming languages: The languages used in robotics systems, including C++, Python, Java, and MATLAB.
Operating systems: The various operating systems used in robotics systems, including Windows, Linux, and Mac OS.
OpenCV: A popular computer vision library used for image and video processing.
Robotics engineering: The study of designing, building, and programming robots for various applications.
Control systems: The study of feedback and control mechanisms used to keep robots stable, precise, and accurate.
Automation: The use of robotics and other technologies to automate manual tasks, such as manufacturing and assembly lines.
Robot kinematics: The study of the motion and movement of robots and how they interact with their environment.
Machine vision: The study of the interaction between machines and the environment, particularly in manufacturing and inspection applications.
Stereovision: The use of two cameras to generate a 3D depth map of a scene.
Sensors: The various sensors used in robotics, including proximity sensors, ultrasonics, and liDARs.
Localization: The process of determining a robot's position relative to its environment.
Mapping: The creation of a detailed representation of an environment using sensors and vision systems.
Path planning: The ability of a robot to navigate a complex environment and plan its actions accordingly.
Human-robot interaction: The study of how humans and robots can interact with each other, including natural language processing, gesture recognition, and virtual reality.
Ethical considerations: The study of ethical issues related to the use of robotics technology, such as privacy, safety, and the impact on the workforce.
2D Vision Systems: These systems use cameras to capture images of 2D objects, such as barcodes or labels, to aid in identification or positioning.
3D Vision Systems: These systems use multiple cameras to capture 3D images of objects, allowing for better detection and localization of objects in 3D space.
Stereo Vision Systems: Similar to 3D vision systems, but using two or more cameras to simulate human binocular vision in order to create 3D images of the environment.
Time-of-Flight Vision Systems: These systems use lasers or infrared sensors to measure the distance between the robot and objects in its environment, allowing for accurate navigation and obstacle avoidance.
Structured Light Vision Systems: These systems use a pattern of projected light to create a 3D image of the environment, allowing for better object recognition and localization.
Motion Vision Systems: These systems use cameras to track and analyze the movements of objects or people, allowing for applications such as security or automated surveillance.
Thermal Vision Systems: These systems use thermal imaging cameras to detect changes in temperature, allowing for applications such as fire detection or tracking of animals.
X-Ray Vision Systems: These systems use X-rays to image parts or components of machines, allowing for non-destructive testing and inspection.
Hyperspectral Imaging Systems: These systems use cameras to capture images across multiple spectral bands, allowing for improved image analysis and detection of differences in materials or substances.
Infrared Vision Systems: These systems use infrared cameras to capture images in low light or no light conditions, allowing for applications such as monitoring of manufacturing processes or inspection of circuit boards.
"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.