03-08-2017, 12:10 AM
Hey if anyone cn help to guide abt GVPP it would be of a great help...
Thank you
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ieee papers on generic visual perception processor pdf
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Hey if anyone cn help to guide abt GVPP it would be of a great help...
Thank you
07-08-2017, 10:11 AM
The generic visual perception processor is a unique chip modeled in the perceptual capabilities of the human brain, which can detect objects in a moving video signal and locate and track them in real time. Imitating the neural networks of human eyes and the brain, the chip can handle about 20 billion instructions per second. This electronic eye on the chip can handle a task that varies from the detection of variable parameters such as in the form of video signals and then processing it for co-Generic visual perception processor is a single chip modeled on the perceptual capabilities of the human brain , Which can detect objects on a moving video signal and then locate and track them in real time. Imitating the neural networks of human eyes and the brain, the chip can handle about 20 billion instructions per second.
The "generic visual perception processor (GVPP)" has been developed after 10 long years of scientific effort. The Generic Visual Perception Processor (GVPP) can automatically detect objects and track their movement in real time. The GVPP, which crosses 20 billion instructions per second (BIPS), models the human perceptual process at the hardware level by imitating separate spatial and spatial space Functions of the eye-to-brain system. The processor sees its environment as a stream of histograms with respect to the location and speed of objects. GVPP has proven to be able to learn in-place to solve a variety of pattern recognition problems. It has automatic normalization to vary object size, orientation and lighting conditions, and can work in daylight or darkness. This electronic "eye" on a chip can now handle most of the tasks that a normal human eye can. That includes driving safely, selecting ripe fruits, reading and recognizing things. Sadly, although modeled on the visual perception capabilities of the human brain, the chip is not really a medical wonder, prepared to cure the blind. GVPP tracks an "object", defined as a certain set of tonality, luminance, and saturation values in a specific form, from frame to frame in a video sequence, anticipating where the direction is and the output edges make "differences" With the background. This means that you can track an object through different light sources or size changes, such as when an object approaches the viewer or moves further. The greatest performance strength of GVPP over current vision systems is their adaptation to varying light conditions. Today's vision systems dictate even shade less lighting and even next-generation prototypes, designed to operate under "normal" lighting conditions, can only be used in the early morning hours at dusk. The GVPP, on the other hand, adapts to changes in real time in lighting without recalibration, day or light. For many decades the field of computing has been trapped by the limitations of traditional processors. Many futuristic technologies have been limited by the limitations of these processors. These limitations come from the basic architecture of these processors. Traditional processors work by cutting each complex program into simple tasks that a processor could execute. This requires the existence of an algorithm for solving 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 works well. It can solve a problem with its neural learning function. Neural networks are extremely fault tolerant. By its design, even if a group of neurons obtain, the neural network only undergoes a mild degradation of the performance. He will not fail abruptly at work. This is a crucial difference, from traditional processors since they do not work, even if some components are damaged. GVPP recognizes stores, parties, and process patterns. Even if the pattern is not recognizable to a human programmer at the entrance of the neural network, it will unearth it from the input.
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