IMAGE TECHNOLOGY
Vision is most efficient in conveying information. The more you know about the past and present, the better you are prepared for the future
W hile talking about the history of digital image processing, the first question that comes to my mind iswhy do we need to analyse memory of something that happened ages ago?
Well, quoting Theodore Roosevelt, The more you know about the past and present, the better you are prepared for the future.
Since vision is most efficient in conveying information, a necessity of processing images was felt. As computers slowly consolidated their prominence in extracting data, there grew a requirement for computers to be able to obtain background data from images.
Facial Landmark analysis with Image Processing
The earliest applications of image processing on digital images can be traced to the 1920s when it was a requirement to improve the quality of images transmitted over a large distance through telegraph. In the 1960s when digital computers became powerful enough to carry out significant data, image processing came into play for the analysis and manipulation of the digitized image, especially in order to improve its quality.
NASAs Ranger 7 is considered to be the first mainstream achievement in the field of digital image processing. The pictures of lunar surface transmitted by Ranger 7 were processed using algorithms to correct various types of image distortion inherent in the on board television camera.
First Image of the Moon taken by a U.S. Spacecraft Ranger 7
The applications of Digital Image Processing began in Biomedical Science after the invention of Computerized Axial Tomography (CAT) by Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack, also known as Computerized Tomography (CT). After the 1980s, the use of digital image processing techniques exploded in various fields including image enhancement/restoration, artistic effects, medical visualization, industrial inspection, law enforcement and human-computer interfaces. 1990 saw the use of digital image processing techniques in correcting the images obtained by Hubble Space Telescope which can take images of very distant objects.
Since then with the advent of machine learning and neural networks, the vision technology is adopted in various fields ranging from industries, biological sciences, geography, astronomy, archaeology, remote sensing, remote sensing, forensic sciences, etc. The development is still in progress with a vision of bright future.
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