By 1957 computers were in common use in many laboratories and commercial establishments. Originally, they were devoted exclusively to numerical, algebraic, and geometric computation. Later, the symbol manipulation capability of computers became recognized, leading to so-called business data processing in which alphanumeric processing became routine. The alphanumeric data presented an obvious problem of inputting the vast quantity of data needed for business. This created activity in developing character recognition machinery (9). It occurred to R.A.Kirsch that a general purpose computer could be used to simulate the many character recognition logics that were being proposed for construction in hardware. This would require an input device that could transform a picture (of a character) into a form suitable for storage in the memory of a computer.
A further important advantage of building such a device was that it would enable programs to be written to simulate the multifarious ways in which humans view the visible world. A tradition had been building in which simple models of human structure and function had been studied, for example, in neuroanatomy and neurophysiology (10). The emphasis on binary representations of neural functions led us to believe that binary representations of images would be suitable for computer input. This serious mistake, discussed below, was implemented in the first picture scanner built. It was connected to the SEAC in 1957 and it enabled Kirsch's group to experiment with algorithms that launched the fields of image processing and image pattern recognition (11).
The scanner used a rotating drum and a photomultiplier to sense reflections from a small image mounted on the drum. A mask interposed between the picture and the photomultiplier tessellated the image into discrete pixels.
The group experimented with several classes of algorithms. The first was homogeneous transformations. Once an image was acquired, the great speed of SEAC was used to transform it with edge enhancement filters. These have become important in recent years as highly parallel methods of processing became common in neural network simulations, for example. They also provided the basis for the large class of image enhancement methods that developed. The group also wrote algorithms to make measurements on objects in an image. By showing that these objects could have multiple connectivity and still be measured correctly, they encouraged the development of specialized machines for image analysis.
A staticizer connected to the SEAC memory enabled a stored image to be displayed on a cathode ray oscilloscope. This made it possible for the researchers to see what the computer "saw". And when they could see binary images, they realized the limitations of binary representation. So they experimented with superimposing multiple scans at different scanning thresholds and the use of time varying thresholds for pulse density modulation to represent multiple gray levels in an image.
A feel for the age and maturity of the image processing field can be seen from the fact that one of the first pictures ever scanned and redisplayed was of Kirsch's newborn son. Today, his face is scanned nightly and digitally processed to appear on the nightly news as a TV reporter. Recently, he showed his newborn daughter on the evening news.<< previous next >>
Exhibit Home | Introduction |
Contributions | Evangelism | Testing | Early Image Processing |
Consequences | Development of Image Processing | New Processing Tools | Conclusion | References