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Generic Visual Perception Processor

By Author – Ashish Kasture

 

The Generic visual perception processor is a single chip modeled on the perception capabilities of the human brain, which can detect objects in a motion video signal and then locate and track them in real time. Imitating the human eyes neural networks and the brain. This chip can handle about 20 billion instructions per second. This electronic eye on the chip can handle a task that ranges from sensing the variable parameters as in the form of video signals and then process it for co-Generic visual perception processor is a single chip modeled on the perception capabilities of the human brain, which can detect objects in a motion video signal and then locate and track them in real time. Imitating the human eyes neural networks and the brain, the chip can handle about 20 billion instructions per second.
Generic Visual Perception Processor can automatically detect objects and track their movement in real-time. The GVPP, which crunches 20 billion instructions per second (BIPS), models the human perceptual process at the hardware level by mimicking the separate temporal and spatial functions of the eye-to-brain system. The processor sees its environment as a stream of histograms regarding the location and velocity of objects. GVPP has been demonstrated as capable of learning-in-place to solve a variety of pattern recognition problems. It boasts automatic normalization for varying object size, orientation and lighting conditions, and can function in daylight or darkness. This electronic “eye” on a chip can now handle most tasks that a normal human eye can.
That includes driving safely, selecting ripe fruits, reading and recognizing things. Sadly, though modeled on the visual perception capabilities of the human brain, the chip is not really a medical marvel, poised to cure the blind. The GVPP tracks an “object,” defined as a certain set of hue, luminance and saturation values in a specific shape, from frame to frame in a video stream by anticipating where it’s leading and trailing edges make “differences” with the background. That means it can track an object through varying light sources or changes in size, as when an object gets closer to the viewer or moves farther away. The GVPP’S major performance strength over current-day vision systems is its adaptation to varying light conditions. Today’s vision systems dictate uniform shadowless illumination and even next-generation prototype systems, designed to work under normal lighting conditions, can be used only dawn to dusk.

The GVPP on the other hand, adapt to real-time changes in lighting without recalibration, day or light. For many decades the field of computing has been trapped by the limitations of the traditional processors. Many futuristic technologies have been bound by limitations of these processors. These limitations stemmed from the basic architecture of these processors. Traditional processors work by slicing each and every complex program into simple tasks that a processor could execute. This requires an existence of an algorithm for the solution of the particular problem. But there are many situations where there is an inexistence of an algorithm or inability of a human to understand the algorithm.
Even in these extreme cases, GVPP performs well. It can solve a problem with its neural learning function. Neural networks are extremely faulted tolerant. By their design even if a group of neurons gets, the neural network only suffers a smooth degradation of the performance. It won’t abruptly fail to work. This is a crucial difference, from traditional processors as they fail to work even if a few components are damaged. GVPP recognizes stores, matches and process patterns. Even if the pattern is not recognizable to a human programmer in input the neural network, it will dig it out from the input. Thus GVPP becomes an efficient tool for applications like the pattern matching and recognition last references are given.