30-08-2017, 02:30 PM
The 'generic visual perception processor (GVPP)' has been developed after 10 long years of scientific effort. The generic Visual Perception Processor (GVPP) can detect objects automatically and track their movement in real time.
GVPP, which crosses 20 billion instructions per second (BIPS), models the human perceptual process at the hardware level by mimicking the temporal and spatial functions separate from the eye-brain system. The processor sees its surroundings 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.
GVPP tracks an "object", defined as a certain set of hue, 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 various light sources or changes in size, such as when an object approaches the viewer or moves further.
The greatest performance strength of GVPP over current day vision systems is their adaptation to varying light conditions. Today's vision systems dictate even shade less lighting, and even the next generation of prototype systems, designed to work in "normal" lighting conditions, can be used only at dawn 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 fine. You can solve a problem with your 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. It will not stop working abruptly. This is a crucial difference, from traditional processors, since they do not work, even if some of the components are damaged. GVPP recognizes stores, matches, and process patterns. Even if the pattern is not recognizable to a human programmer at the entrance of the neural network, it will dig it out of the input.
GVPP, which crosses 20 billion instructions per second (BIPS), models the human perceptual process at the hardware level by mimicking the temporal and spatial functions separate from the eye-brain system. The processor sees its surroundings 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.
GVPP tracks an "object", defined as a certain set of hue, 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 various light sources or changes in size, such as when an object approaches the viewer or moves further.
The greatest performance strength of GVPP over current day vision systems is their adaptation to varying light conditions. Today's vision systems dictate even shade less lighting, and even the next generation of prototype systems, designed to work in "normal" lighting conditions, can be used only at dawn 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 fine. You can solve a problem with your 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. It will not stop working abruptly. This is a crucial difference, from traditional processors, since they do not work, even if some of the components are damaged. GVPP recognizes stores, matches, and process patterns. Even if the pattern is not recognizable to a human programmer at the entrance of the neural network, it will dig it out of the input.