Vision: scene recognition, object recognition, face recognition.
Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information. A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image. 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. Computer vision has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception.
Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications.
As a scientific discipline, 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, or multidimensional data from a medical scanner.
As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems. Examples of applications of computer vision include systems for:
Controlling processes, e.g., an industrial robot;
Navigation, e.g., by an autonomous vehicle or mobile robot;
Detecting events, e.g., for visual surveillance or people counting;
Organizing information, e.g., for indexing databases of images and image sequences;
Modeling objects or environments, e.g., medical image analysis or topographical modeling;
Interaction, e.g., as the input to a device for computer-human interaction;
Automatic inspection, e.g., in manufacturing applications.
Sub-domains of computer vision include: scene reconstruction, event detection, video tracking, object recognition, learning, indexing, motion estimation, and image restoration.
Robotics, car driving.
Robotics is the branch of technology that deals with the design, construction, operation, development and application of robots and computer systems for their control, sensory feedback, and information processing. These technologies deal with automated machines that can take the place of humans, in dangerous or manufacturing processes, or simply just resemble humans. Many of today's robots are inspired by nature contributing to the field of bio-inspired robotics.
The concept in creation of machines that could operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century. Throughout history, robotics has been often seen to mimic human behavior, and often manage tasks in a similar fashion. Today, robotics is a rapidly growing field, as technological advances continue, research, design, and building new robots serve various practical purposes, whether domestically, commercially, or militarily.
Artificial Intelligence, Vision & Robotics (The introduction of Yale University research into AI, 2012)
One of the most interesting AI areas is sensor-controlled behavior, in which a machine acts in the real world using information gathered from sensors such as sonars and cameras. This is a major focus of AI research at Yale. The difference between sensor-controlled behavior and what computers usually do is that the input from a sensor is ambiguous. When a computer reads a record from a database, it can be certain what the record says. There may be philosophical doubt about whether an employee’s social-security number really succeeds in referring to a flesh — and — blood employee but such doubts don’t affect how programs are written. As far as the computer system is concerned, the identifying number is the employee, and it will happily, and successfully, use it to access all relevant data as long as no internal inconsistency develops.
Contrast that with a computer controlling a soccer-playing robot, whose only sensor is a camera mounted above the field. The camera tells the computer, several times per second, the pattern of illumination it is receiving encoded as an array of numbers. (Actually, it’s three arrays, one for red, one for green, and one for blue.) The vision system must extract from this large set of numbers the locations of all the robots (on its team and the opponent’s) plus the ball. What it extracts is not an exact description, but always noisy, and occasionally grossly wrong. In addition, by the time the description is available it is always slightly out of date. The computer must decide quickly how to alter the behavior of the robots, send them messages to accomplish that, and then process the next image.
The reasons Yale scientists choose to work in such a perversely difficult area are the following: first, one ultimate goal of A.I. research is to understand how people are possible—i.e., how it is that an intelligent system can thrive in the real world. The vision and other senses are so good that it is sometimes possible to overlook the noise and errors they are prone to, when in fact we are faced with problems that are similar to the robot-soccer player, but much worse. We will never understand human intelligence until we understand how the human brain extracts information from its environment, and uses it to guide behavior. Second, vision and robotics have many practical applications. Space exploration is more cost-effective when robots are the vanguard, as demonstrated dramatically by the Mars Rover mission of 1997. Closer to home, we are already seeing commercially viable applications of the technology. For instance, TV networks can now produce three-dimensional views of an athletic event, by combining several two-dimensional views, in essentially the same way animals manage stereo vision. There is now a burgeoning robotic-toy industry, and we can expect robots to appear in more complex roles in our lives. So far, the behaviors these robots can exhibit are quite primitive. Kids are satisfied with a robot that can utter a few phrases or wag its tail when hugged. But it quickly becomes clear even to a child that today’s toys are not really aware of what is going on around them. The main problem in making them aware is to provide them with better sensors, which means better algorithms for processing the outputs from the sensors.
Research in this area at Yale is carried out by the Center for Computational Vision and Control, a joint effort of the Departments of Computer Science, Electrical Engineering, and Radiology.