28-06-2012, 12:02 PM
machine vision
INTRODUCTION
The role of a machine vision is to create a model of real world from images. A system recovers useful information about a scene from its two-dimensional image to three-dimensional projections. Since image is two-dimensional projection of three-dimensional world, the information is the not directly available and must be recovered. This recovery requires the inversion of a many to many mapping. To recover the information, knowledge about the object in the scene and projection geometry is required.
Machine vision (MV) is the process of applying a range of technologies and methods to provide imaging-based automatic inspection, process control and robot guidance in industrial applications. While the scope of MV is broad and a comprehensive definition is difficult to distil, a "generally accepted definition of machine vision is '... the analysis of images to extract data for controlling a process or activity.
The eye is one of the most important organs of the human body and our skills greatly depend on our ability to see, recognise and distinguish objects and to estimate distances. Most jobs depend on our ability of visual perception. As amazing as the human sense of vision may be, we must acknowledge that today's production technologies more and more often extend well beyond the limits of human visual capacities. This is where machine vision technology comes in.
What do we actually mean when we talk about machine vision or vision technology? It does not mean graphics or design: instead, it concerns the technology of artificial vision. Cameras and computers give machines or devices the ability to actually "see", to recognise objects or situations and to make the right decisions accordingly.
Consider the following definition:
"Vision technology is still a relatively young discipline, which had its breakthrough in the early 1980s. It deals with images or sequences of images with the objective of manipulating and analysing them in order to a) improve image quality (contrast, colour, etc.), b) restore images (e.g. noise reduction), c) code pictures (data compression, for example) or d) understand and interpret images (image analysis, pattern recognition). Thus vision technology can be applied wherever images are generated and need to be analysed: in biology (counting cells), in medicine (interpreting CT scanning results), in the construction industry (thermo graphic analysis of buildings) or in security (verification of biometric dimensions). Vision technology is an interdisciplinary technology that combines lighting, optics, electronics, information technology, software and automation technology. Machine vision refers the industrial application of vision technology. It describes the understanding and interpretation of technically obtained images for controlling production processes. It has evolved into one of the key technologies in industrial automation, which is used in virtually all manufacturing industries.
The beginnings of machine vision were developed in the late 1940s and early
1950s with initial research into artificial intelligence. This is also when the military
Began applying image analysis. This concept did not become industrialized until the
1960s and 70s. At this point, Massachusetts Institute of Technology developed an image
Analysis system that could control a robotic arm for applied industrial uses. In the 1980s,
Machine vision took off and saw great expansion on the industrial level. At this point,
Greyscale machine vision algorithms, single board image processors, and cameras for
Industrial applications became commercially available. Machine vision became a
Production line staple in many industries.
The 1990s brought a boom of growth to the machine vision industry. The
Advancement of computer technology was the main driver behind this expansion.
Processing chips made it possible to create smart cameras, which can not only collect the
Image data, but it can also extract information from these images without using a
Computer or other external processing unit. In the 2000s and continuing on in present
day, the machine vision industry continues to see growth. The availability and
Affordability of digital camera systems greatly increased the accuracy and abilities of machine vision. Because of these advances in technology, machine vision has ceased
being a futuristic idea and is now widely accepted and used within the manufacturing
Industry for applications including quality assurance and control.