Table Of Contents

Introduction: Purpose of the topic
Description
General analysis
Actualization
General recommendations
Conclusion
References

Introduction: Purpose of the topic

At the end of this course, students would have learnt the introduction of computer vision with digital image processing. The goal of this course is to teach image representation, transforms, filtering, compression, boundary detection, and pattern matching. The goal of this course is to teach image representation, transforms, filtering, compression, boundary detection, and pattern matching. 

Description

Humans and animals use their visual abilities to navigate the world, forage for food, and survive. Is it possible to replicate some of these abilities on a computer so that they can assist us and enhance our quality of life by being an active component of our day to day life?  This is next generation science and technology.

Computer vision is the study and application of methods which allow computers to "understand" image content or content of multidimensional data in general. The term "understand" means here that specific information is being extracted from the image data for a specific purpose: either for presenting it to a human operator or for controlling some process. The image data that is fed into a computer vision system is often a digital gray-scale or colour image, but can also be in the form of two or more such images.

Computer vision, on the other hand, studies and describes technical system which are implemented in software or hardware, in computers or in difital signal processors.
Computer vision is by some seen as a subfield of artificial intelligence where image data is being fed into a system as an alternative to text based input for controlling the behaviour of a system. Some of the learning methods which are used in computer vision are based on learning techniques developed within artificial intelligence. 

General analysis

Another way to describe computer vision is in terms of applications areas. One of the most prominent application fields is medical computer vision or medical image processing. This area is characterized by the extraction of information from image data for the purpose of making a medical diagnosis of a patient. Typically image data is in the form of microscopy images, X-ray images and so on. For instance information which can be extracted from such image data is detection of tumours and other malign changes. It can also be measurements of organ dimensions, blood flow, etc. This application area also supports medical research by providing new information, e.g., about the structure of the brain, or about the quality of medical treatments

As it is applied in many fields, another example is in the industry where information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects.

Military applications are probably one of the largest areas for computer vision, even though only a small part of this work is open to the public.

One of the newer application areas is autonomous vehicles which ranges from submersibles, land-based vehicles (small robots with wheels, cars or trucks) to aerial vehicles.

Actualization 

Since a camera can be seen as a light sensor, there are various methods in computer vision based on correspondences between a physical phenomenon related to light and images of that phenomenon. For example, it is possible to extract information about motion in fluids and about waves by analyzing images of these phenomena. Also, a subfield within computer vision deals with the physical process which given a scene of objects, light sources, and camera lenses forms the image in a camera.

Digital v. Analog world

No film is perfectly continuous, hence there is a limit to the resolution of the camera

Aliasing: When a system samples a frequency that is more than half the sampling frequency.

• The result is a ghost signal that does not actually exist
Resolution, and and thus sampling and aliasing, affect imaging in three different ways
• Spatial resolution: the size of photo elements on the sensor
• A fine pinstripe suit will often flicker on TV
• Spectral resolution: the number of samples in the EM spectrum per pixel
• An object can look one color in one condition, and another in a different condition
• Time resolution: the number of images gathered per second
• Prime example is a wagon wheel in the movies: forwards, then backwards
• Can get this effect on the freeway by looking at the tires of moving vehicles

When processing images for a human observer, it is important to consider how images are converted into information by the viewer. Understanding visual perception helps during algorithm development.
Image data represents physical quantities such as chromaticity and luminance. Chromaticity is the color quality of light defined by its wavelength. Luminance is the amount of light. To the viewer, these physical quantities may be perceived by such attributes as color and brightness.

How we perceive color image information is classified into three perceptual variables: hue, saturation and lightness. When we use the word color, typically we are referring to hue. Hue distinguishes among colors such as green and yellow. Hues are the color sensations reported by an observer exposed to various wavelengths 

General recommendations

My recommendations would have been to improve on the research going on in the field of Image Processing and Computer Vision but as we are well aware, it has been growing at a fast pace. The growth in this field has been both in breadth and depth of concepts and techniques. Computer Vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing, and nano-technology to multimedia databases.  

Conclusion

The field of computer vision can be said to be immature and diverse. Even though earlier work exists, it was not until the late 1970's that a more focused study of the field started when computers could manage the processing of large data sets such as images. However, these studies usually originated from various other fields, and consequently there is no standard formulation of the "computer vision problem". Also, and to an even larger extent, there is no standard formulation of how computer vision problems should be solved. Instead, there exists an abundance of methods for solving various well-defined computer vision tasks, where the methods often are very task specific and seldom can be generalized over a wide range of applications. Many of the methods and applications are still in the state of basic research, but more and more methods have found their way into commercial products, where they often constitute a part of a larger system which can solve complex tasks (e.g., in the area of medical images, or quality control and measurements in industrial processes

References

Abdelouahab, M., Khier, B. and Nabila, F. (2005) University Ferhat Abbas of SETIF – ALGERIA

Anil, J. and Thomas, K. (2000) Fundamentals of Digital Image Processing.

David, F. and Jean, P. (2003). Computer Vision, A Modern Approach, Prentice Hall.

http://en.wikipedia.org/wiki/Computer_vision

http://elcvia.cvc.uab.es/journal/publish.php?art=a2005029-3

http://elcvia.cvc.uab.es/public/articles/0504/a2005013-2-art.pdf

http://gtresearchnews.gatech.edu/newsrelease/bees.htm

http://www.netnam.vn/unescocourse/computervision/comp_frm.htm

http://www.computing.dundee.ac.uk/ac_research/themedetails.asp?id=14

Linda, S. and George, S.(2001). Computer Vision, Prentice Hall.

Milan, S., Vaclav, H. and Roger, B. (1999). Image Processing, Analysis, and Machine Vision, PWS Publishing.

Richard, H. and Andrew, Z. (2003). Multiple View Geometry in computer vision, Cambridge University Press.

Tim, M. (2004). Computer Vision and Image Processing, Palgrave Macmillan.