The data sensors used to gather data for computers often exceed human ability. For example, computers can easily sense sound beyond 100 kilohertz, but humans hear less than 20 kilohertz. Furthermore, many data sensors, such as accelerometers and humidity sensors, are available for computers that are not part of the normal human repertoire. In short, the computer is very good at gathering highly accurate data from a growing list of sensors, but all of the data is delivered to the computer in raster. Therefore, the computer can do very little with it.

On the other hand, mathematics is so basic to computers that most of the early uses of computers concerned mathematical calculations. There are literally hundreds of ways to manipulate mathematical data on a computer. For example, if a picture is in vector (i.e., mathematical formulas), a ray tracing algorithm can be run on it to accurately change the level of light, but ray tracing cannot be done with raster data.

This ability of vector and inability of raster can be extended to many other algorithms that include animation, simulation, condensation (a form of filtering), synchronization (frequency adjustment), perspective (creating a 3-D model from 2 or more pictures), and resolution enhancement.

Almost all computer algorithms need mathematics to operate, but practically all data is gathered in raster. Without a raster to vector converter computers are deaf, blind and unfeeling and cannot taste or smell. With a raster to vector converter all of these senses and many more are open to the computer and the ability of the computer to play a more useful role is greatly enhanced. Since computers are a $2 trillion market without a raster to vector converter, one can hardly imagine the economic impact of making computers much more useful, but it is likely that the market would triple. Many would predict even stronger gains than that. Therefore, a raster to vector converter would propel some of the most dramatic economic growth ever recorded.

For example, in education, computers are currently only a handy tool. Various educational and edutainment titles have been created to help children learn. The greatest obstacle is that the computer cannot understand the child’s speech. If computers could understand speech, they could help with phonetics, spelling, reading, grammar, and all basic word skills. The computer would become an interactive textbook that could give its full attention to an individual student. Therefore, feedback, repetition, and other educational parameters, which are required for accelerated learning, can be optimized. Compare this futuristic vector enabled environment to the current keyboard limited tool of today.

A raster to vector converter gives the computer the ability to interpret its surroundings. It is not limited to education alone, rather it extends to almost every other industry. The impact and promise to the world could hardly be overstated. For example, in 25 years time, it could increase the standard of living many times over because there would not be any limit to industrial productivity, except resources. Nearly all industrial tasks could be easily automated.

By now, a number of people claim to have raster to vector converters with wavelets, fractals, and Bezier curves counted among the most popular. Technically, these claims are correct (raster data is converted to vector), but practically the proposed solutions do not yield mathematical formulas that allow real world solutions.

In going back to the heart of the problem, we can understand why these current solutions are inadequate. In other words, what makes the problem of finding a real world solution so difficult?

The larger part of the problem is that there are too many dots, and the smaller part of the problem is that the algorithm needs to deal with the way that the dots were collected.

Let’s start with the small part of the problem first. If our sensory system had one sensor, we could tell whether something was there or not. We could not tell whether it was a fly or an elephant, but we could make a good guess from the color of the object. If the sensor was only one-third covered with the fly and the background was white, the color mix would be a light gray (assuming it is a black fly). So color alone is not reliable.

If we had 16 sensors we could do better and if we had 512 sensors we could tell the fly from the elephant. With 512 sensors we would not have a very smooth picture of the elephant and we would need to fill between the data points. The fill should take into account the color from the sensors that are completely immersed in the image and it should extrapolate the points between the data points from the partially covered sensors. This is how scanners yield more data points with greater accuracy than what the scanner actually senses.

In other words, the sensors work in a specific and unvarying way and by knowing this we can take advantage of the partially sensed objects to accurately project the actual image. Therefore, by using color and integrating shapes, we can solve the smaller part of the problem.

Before we can solve the bigger part of the problem we have to be able to write formulas across a large number of dots. Mathematics is only able to write equations over a few dots. For example, with 1 dot there is one shape. With 9 dots arranged into a 3 by 3 array, there are about 21,000 possible shapes. The inclusion of each additional dot takes the number of possibilities up exponentially. Each case has to be considered and it must have a solution. There are many problems in mathematics that don’t have solutions. For example, only about 33% of second order partial differential equations have solutions. Therefore, mathematics alone will not solve the problem.

Purely mathematical approaches, such as wavelets and fractals, are able to convert raster to vector by limiting the size of the converted picture to about 5 by 5 dots (this is actually generous). Large pictures are converted by converting thousands of independent and disconnected small pictures. All other methods do something similar. The very idea of converting a 10 by 10 dot picture with all the attendant possibilities seems ludicrous. Real world pictures are often 2,000 by 2,000 dots. Therefore, a real world solution seems hopeless.

On the other hand, equations running over 5 dots or so (in some cases) do not make the solution impractical for computer algorithms. The problem comes when adjacent equations are independent. If an interrelationship between equations can be found, a number of equations (related together) are a practical solution. Furthermore, systematic philosophy predicts that such relationships must exist in every case. In this philosophical system (from Aristotle and Saint Thomas of Aquinas, among others), everything is made up of parts. For example, skin is made of cells. Cells are a composition of molecules. Molecules are the products of atoms, and there is no smallest part. The process goes on and on.

For our part, we need to manipulate data. Manipulation of nature can be undertaken by understanding the lower levels of recursion. For example, we can improve crops by working with the genetic material that the crops come from. Therefore, if we could find the parts that made up a picture, we could build a practical raster to vector converter. From a theoretical perspective, we completed this task in the six years from 1986 to 1992.   Since then, we have been implementing the solution.

Even this philosophical approach is not unique to us. Other people have tried it, but finding all of the data possibilities is a daunting task. Finding one or two component parts will not yield a functional product. Every case has to be discovered, analyzed and related to every other case. It is not a certainty that the project would be completed in even 500 years.

The basic nature of the work deals with complex digital logic. Humans, yes even engineering nerds, are not well suited for this and the task is unpleasant. Since the logic is extremely complex, a high caliber digital designer would be required. These types of people are in high demand and they don’t need to do things like this to make a living.

Therefore, a capable engineer who has other options, has to enter an unpleasant task of indeterminable length to even try to find the solution. How would such a project be funded with no one to do it and not enough time to devote to it?

For these reasons, we do not expect and have not found another group working on this approach with a long-term effort.

If the vector data’s resolution can be changed in a smooth, unified way, then all of the equations are necessarily interrelated. Resolution enhancement is a real world algorithm that can be used to test for a practical solution.

An impractical solution is one that breaks the conversion into a number of steps. When the resolution is changed, the picture will lack continuity. If we were to compare the quest of a raster to vector converter with that of flight, the other methods are similar to executing the standing long jump several hundred times to get there. The entire length in the air of all the jumps together might be equivalent to what the Wright Brothers accomplished at Kitty Hawk, but all of the jumps lack the continuity of a single flight.

In the same way, Demo 4 was the first time that any raster to vector converter accurately converted lines and other shapes with no lack of continuity. (Please see the IFR Development section of our White Paper.) From a human perspective, we might see the shapes in Demo 4 as somewhat trivial, but not many humans, even when given several hours, can determine that there are 8 diagonal lines in Test Pattern 9 (see demonstration paper). To most humans, it seems that there are 5 diagonal lines. IFR not only found 8 lines, but it exactly located the beginning and end of each line. Therefore, IFR already demonstrates imaging ability that exceeds nearly all humans. This is one of the reasons that Demo 4 is much more dramatic than Demo 7 (alpha).

IFR is the only raster to vector converter that can change the resolution of all the test patterns in Demo 4 without errors. Not only does the closest competition have about 100 errors, a completely new approach, such as IFR, is required before much improvement can be made. The standing long jump is not the same as riding in an airplane. We wouldn’t call Demo 4 a flight across the Atlantic, but we would call it an historic flight along the beach.

Demo 4 makes a strong case that we have the basic solution implemented and only the full debug stands between us and a product. Furthermore, we have most of the   patterns working in Demos 5 and 6 and we don’t expect the others to take very long. The writing is on the wall. It won’t be long before we have a raster to vector converter that can convert any picture. Demo 4 is proof that we have moved beyond concept, that we have implemented the technology and now only need to continue to develop a product.