Technology

Eye Catching

Perhaps the most remarkable aspect of Sightech’s products is their ability to learn objects simply by looking at them. Learning continues unhindered even if the object is moving or rotating. What’s most impressive is that Sightech’s products can learn rotating objects in just a few minutes without any programming.

Another dramatic feature is how quickly Sightech’s products make decisions. For example, Eyebot can make up to 60 decisions in a single second. Eyebot can recognize objects it has learned in cluttered backgrounds, or identify defects quickly without strobe lighting.

Neural Net/Fuzzy Logic Attributes

Sightech's technology, therefore, is based on how yellow jackets (and other organisms) see, learn, and navigate. This results in a self-learning system that does not require any programming. Instead, the system "programs" itself when it sees new objects. The more you teach it, the better it gets. This is the concept behind neural networks: repetition reinforces learning.

Inspired by Yellow Jackets

The yellow jacket bee inspired Art Gaffin, Sightech’s CEO and CTO, by presenting him with a difficult puzzle: How can yellow jackets see when they have a tiny brain, little processing power, minimal memory, and crude visual sensors?

Mr. Gaffin’s conclusion was simple: it’s not that machine vision does not have sufficiently powerful hardware (because yellow jackets clearly do not), it’s just that we do not know nature’s algorithm.

Therefore, more than 15 years ago Mr. Gaffin started an ambitious quest: to understand and decode nature’s algorithm for vision and learning. Nine years ago, Brad Smallridge teamed with Mr. Gaffin to produce disarmingly easy-to-use and powerful machine vision systems. Thus, Neuro-RAM was born in Mr. Gaffin's garage in Silicon Valley.

Learns Objects/Features, Not Pixels

Sightech's Neuro-RAM algorithm extracts learns, and inspects extremely small visual features. In fact, it learns up to 20 million features a second. Moreover, it learns these features in relationship to each other. The processing going inside the black box is simply astounding.

What is a feature? Generally, features are very small shapes, such as a segment of line, circle, or squiggle. A simple way to visualize a feature is to look at a human face and zoom into a part of the face, say, the ear. You will notice that there are many little lines and shapes that make up the concept of an ear.

Eyebot doesn't store the picture of the ear but instead compiles a massive database of features in association with other features. During the RUN process, Eyebot looks at 12 million features a second and compares them with the database of features it has in its brain. If it sees a new feature, it decreases the Score slightly. The more drastic the new feature is, the more the Score drops. Similarly, whenever many new features occur, the Score will drop precipitously.

The new feature may be a new shape that Eyebot never learned or an old shape seen in a new way (e.g., rotated or in a new relationship with another feature). When two features are near each other, they create a third feature. If this third feature is not part of the "known feature database," then Eyebot will signal it.