Computer Vision Processing

Computer Vision Processing is the technology of any machines which can observe outer world through a source. Here observe can be defined as machine is able to extract information from any images that is necessary to solve out tasks. The computer vision is concerned with the theory behind artificial systems that extract data from images. The image can be taken from multiple sources such as video sequences, from multiple cameras, scanners etc.

Image Processing

Image processing is any form of signal processing where the input can be any image such as photograph or a video frame and the output can be an image or a set of characters. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

The most common type of image processing is photography. In this process, an image is captured using a camera to create a digital or analog image. In order to produce a physical picture, the image is processed using the appropriate technology based on the input source type.

In digital photography, the image is stored as a computer file. This file is translated using photographic software to generate an actual image. The colors, shading, and nuances are all captured at the time the photograph is taken the software translates this information into an image.

There are three major benefits to digital image processing. The consistent high quality of the image, the low cost of processing and the ability to manipulate all aspects of the process are all great benefits. As long as computer processing speed continues to increase while the cost of storage memory continues to drop, the field of image processing will grow.

There are three major benefits to digital image processing. The consistent high quality of the image, the low cost of processing and the ability to manipulate all aspects of the process are all great benefits. As long as computer processing speed continues to increase while the cost of storage memory continues to drop, the field of image processing will grow.

Image Analysis

Image Analysis is the extraction of meaningful and important factors from images, mostly from digital images using image processing techniques. Tasks such as reading bar codes or identifying a person’s face can be performed by Image Analysis.

There are many different techniques used in automatically analysing images. Each technique may be useful for a small range of tasks, however there still aren’t any known methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human’s image analysing capabilities. Types of tasks can be edge finding, object localisations, motion tracking etc.

Essentially, image analysis is simply data analysis with the initial raw data stored in a spatially referenced array. The array is usually thought of as representing a two-dimensional, lens-based visual record, though this isn’t necessarily so. Reading a bar code is the frequently given bottom-end example of a one dimensional image designed for analysis.

Image analysis covers a lot of territory, these days. From astronomy to microbiology, if there is a field of study which remains immune then hasn’t found it. Some of the glamorously obvious applications which it underpins are in biometrics, where it is used in fingerprint readers and facial recognition systems.

Image Understanding

Image understanding is the process of actually interpreting those regions/objects to figure out what’s actually happening in the image. This may include figuring out what the objects are their spatial relationships to each other, etc. It may also include ultimately making some decision for further action.

Basically, it is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent.

Image understanding also describes the concept of understanding silent movies which requires more than object recognition, common sense, emotions and sense of humour.

The computer vision processing is a general term which includes the fields like image processing, image analysis and image understanding. It is also called a Machine Vision, where image processing is used in a wide range technology. Computer vision processing is a relatively new science. Research from a variety of fields slowly evolved and formed into a new branch – Computer vision.


Computer Vision <> Biological Vision

Computer vision systems are used for controlling processes like any industrial robot, autonomous vehicle – car, bike, plane to execute them on user commands and sense according to situations. The technology processes detection of events such as visual surveillance or people counting through the video capture. Computer Vision processing studies and describes artificial vision systems implement in software or hardware, however biological vision where visual perceptions of human and animals resulting in models how these system operates in terms of physiological processes.

Computer vision is a field of study and research in computer science and engineering that focuses on computers and machines that can receive and interpret visual data. The concerns of this field can be as simple as devising and integrating cameras that work well with computers or as complex as developing visual systems that enable computer technologies to interact with users. While there are many different potential applications for computer vision, medical technology has become one of the most practical and accessible fields for the implementation of such visual technology. Highly detailed images of patients can provide a great deal of valuable diagnostic data that leads to highly personalized and useful medical data.

There are many different elements of computer vision that often must be combined to make a cohesive and useful vision system. In cases that require anything more complicated than reproducing an image, for instance, some level of image recognition or detection is usually required. Computer vision technology is designed to recognize specific visual cues, such as those on human faces, in order to focus on or track a given object. Some technologies are designed to recognize text, often with the purpose of “translating” the text from an image file to a text file that can be edited and manipulated.

Computer vision is commonly studied in conjunction with biological vision, the process by which organisms such as humans receive and interpret visual data. The two fields of study contribute significantly to each other. Advances and developments in computer vision can suggest possible mechanisms by which biological vision occurs. Discoveries in biological vision, on the other hand, can provide ideas for new ways for computer technology to handle external visual data. It is not uncommon for biologists, computer scientists, and engineers to work together on projects regarding computer or biological vision.

There are many different fields, most within the sciences, that make regular use of computer vision technology, usually for research purposes. Artificial intelligence, a common area of study in computer science and engineering, uses visual technology to devise navigation or recognition systems for robotics. Computer vision technology is sometimes used in optics because artificial visual systems can be made to “see” and record a wider range of visual data than organic visual systems can. Additionally, many different fields contribute to the development and implementation of visual technology in computerized systems. Mathematics, for instance, is an essential element of the programming that goes into the interpretation of visual data by computers.

Computer Vision Processing with new Science

Computer vision is intimately tied to:

Artificial Intelligence
A human cannot act without first using his senses to determine a course of action. In the case of vision, light that enters the eyes is then analyzed and interpreted by the brain. In the same way, the first step to advancement in Artificial Intelligence is to sense and make sense. Computer Vision is used to allow AI to identify and analyze a picture or components in a picture taken by sensory instruments. After identifying edges, objects, or patterns the AI can then act according to the stimulus given.

Computer vision is a tool used in physics to extend our understanding beyond things that we cannot physically see. For instance, radiation beyond the spectrum of visual light needs to be processed in a form that makes sense to human brains. Complex computer vision algorithms are used in the image formation process. For example, scientists can specify parameters that will show the gas helium in one color and the gas hydrogen in another color. Larger concentrations will result in higher saturation of color. This provides a visual way for scientists to analyze a cloud of gas and its composition.

Neurobiology and Optics
Computer vision was created by humans and consequently, is tied closely to the research of human vision. Some researchers believe that human vision is similar to an extremely complex version of computer vision. Human vision, like computer vision, is bound by rules – a whole lot of them. Though computer vision today is nowhere near the complexity of human vision, it’s used to mimic and simulate the behavior of biological optics.

Advances in the field of computer vision can lead to the advancement of military technologies. Imagine a world where computers monitor every inch of the sky 24/7 and 365 days a year. Computer systems identify an object in airspace as a missile and immediately trigger anti-missile systems. Also, imagine the world of super-suits where human vision and computer vision come together to form a new sense of vision never witnessed before. Obviously, computer vision is an exciting and rapidly expanding field which may lead to marvelous things.

Extended as OpenCV

Open Source Computer Vision (OpenCV) is an open source computer programming library developed to support applications that use computer vision. It provides hundreds of functions for the capture, analysis, and manipulation of visual data and can eliminate some of the hassle programmers’ face when developing applications that rely on computer vision. Portions of the library also provide user interface and pattern recognition functions. OpenCV has been employed in both practical and creative applications including self-piloting vehicles and new forms of digital art.

Programming libraries provide common functions or complex capabilities that developers can use in their programs. The OpenCV library contains hundreds of functions that support the capture, analysis, and manipulation of visual information fed to a computer by webcams, video files, or other types of devices. Simple functions might be used to draw a line or other shape on a screen, while the more advanced portions of the library contain algorithms for detecting faces, tracking motion, and analyzing shapes. Many of this library’s algorithms are related to specific uses of computer vision including product inspection, medical imaging, robotics, facial and gesture recognition, and human-computer interaction (HCI). As an open source programming library, OpenCV can be used with very few restrictions in both commercial and hobbyist projects.

With OpenCV a developer can eliminate some of the complex and tedious work that goes into making computer vision function reliably and focus on building the application. Rather than creating algorithms for facial recognition and the like, a programmer can add just a few lines of code to have a program access the appropriate library function. It also means a programmer does not need to master every aspect of computer vision to build a program that uses it.

In addition to the core video and image processing functionality, OpenCV contains secondary modules intended to support other areas of an application. One of these modules includes machine learning algorithms that can analyze and predict visual patterns. The HighGUI module provides user interface elements as well as functions for storing and accessing video and image files.

The OpenCV library can be found at the heart of some vary ambitious projects. Along with an assortment of sensors, computer hardware, and custom tailored software, it powered a heavily modified sport utility vehicle that navigated a 132 mile (212 km) desert race course without human intervention. Not all projects that rely on the library’s resources are so practical, however. Some members of the creative coding movement, a loose confederacy of people who view programming as a form of expression, have used the library to create new forms of digital art. Others have hacked existing devices containing cameras and opened up new possibilities for gaming, interactive computing, and even telepresence.

Modern Applications with Computer Vision Processing

One of the most prominent application fields is medical computer vision or medical image processing. Generally, image data is in the form of microscopy images, X-ray images, angiography images, ultrasonic images, and tomography images. A second application area in computer vision is in industry. Here, information is extracted for the purpose of supporting a manufacturing process. The obvious examples are detection of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as battlefield awareness, imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions.

One of the newer application areas is autonomous vehicles, which include submersibles, land-based vehicles like small robots with wheels, cars or trucks, aerial vehicles, and unmanned aerial vehicles (UAV). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer vision based systems support a driver or a pilot in various situations. Examples of supporting systems are obstacle warning systems in cars, and systems for autonomous landing of aircraft. There are ample examples of military autonomous vehicles ranging from advanced missiles, to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision such as NASA’s Mars Exploration Rover.

Future with Computer Vision Processing

Computer vision and image processing will impact our lives in a number of ways over the coming decades, particularly in the areas of security, robotics, user interface design and entertainment. The UK has one of the highest concentrations of video surveillance cameras in the world. The need to prevent crime coupled with the need to protect the right to privacy of individuals are important topics that concern us all, particularly with the increased risk from terrorism. The large number of cameras means that automated surveillance is required, and computer vision systems are becoming increasingly sophisticated. The need to reconstruct the appearance of an individual, possibly from multiple cameras and at different times also requires sophisticated computer graphic techniques. These can be extended to include adjusting the age, body weight or other parameters for people who have been missing or wanted for some time. This exhibit will attract the user’s attention with a fun interactive Face Transforming tool, and then aim to explain the computer vision and graphics technology behind this fun tool with additional interactive exhibits.

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