Way-2C Information

WAY-2C has been proving itself since 1992 in a wide variety of challenging industrial, government, and military inspection and process control applications. If you are disappointed with the performance of your present color machine vision system, and are serious about wanting to improve it, contact us. We'll give you an honest, no obligation, appraisal of whether WAY-2C can help. Then, if you choose, we'll work with you to see how a WAY-2C system can be implemented with the minimum possible disruption of your existing inspection process and the maximum use of your present hardware and software. Why don't other machine vision companies use the same methods as WAY-2C? We can only speculate, but it seems to us that there are several reasons: different origins, lack of familiarity with the methods and cost of new code and interface development. Traditional color machine vision developed naturally and incrementally from monochrome vision along a perfectly sensible path building on knowledge from printing, video camera and video display technologies. The focus was on individual pixels using well established mathematical tools familiar to all engineers. The methods, suitable for measurements characterized by normal (Gaussian) distributions, work reasonably in situations when color distributions are simple and reasonably Gaussian. Unfortunately, when faced with the need to classify based on complex color distributions the same tools tend to give unreliable results and/or be awkward to use. WAY-2C originated in a need for the military to characterize and understand very small earth motions containing many steps, spikes and discontinuities separated by periods of monotonous drift. These geophysical motion distributions were far from Gaussian so use of the familiar mathematical tools immediately led to wildly erroneous results. After considerable research and some serendipity it was realized that the solution lay in abandoning the use of tools appropriate for Gaussian distributions and concentrating only on those that ...