23-08-2011, 10:16 AM
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1 INTRODUCTION
While computing technology is growing in leaps and bounds, the human brain continues to be the world's fastest computer. Combine brain-power with seeing power, and you have the fastest, cheapest, most extra ordinary processor ever-the human eye. Little wonder, research labs the world over are striving to produce a near-perfect electronic eye.
The 'generic visual perception processor (GVPP)' has been developed after 10 long years of scientific effort. Generic Visual Perception Processor (GVPP) 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.
2 BACKGROUND OF THE INVENTION
The invention relates generally to methods and devices for automatic visual perception, and more particularly to methods and devices for processing image signals using two or more histogram calculation units to localize one or more objects in an image signal using one or more characteristics an object such as the shape, size and orientation of the object. Such devices can be termed an electronic spatio-temporal neuron, and are particularly useful for image processing, but may also be used for other signals, such as audio signals. The techniques of the present invention are also particularly useful for tracking one or more objects in real time.
It is desirable to provide devices including combined data processing units of a similar nature, each addressing a particular parameter extracted from the video signal. In particular, it is desirable to provide devices including multiple units for calculating histograms, or electronic spatio-temporal neuron STN, each processing a DATA (A), by a function in order to generate individually an output value.
The present invention also provides a method for perception of an object using characteristics, such as its shape, its size or its orientation, using a device composed of a set of histogram calculation units.
Using the techniques of the present invention, a general outline of a moving object is determined with respect to a relatively stable background, then inside this outline, elements that are characterized by their tone, color, relative position etc. are determined. .
3 POTIENTIAL SIGHTED
The GVPP was invented in 1992, by BEV founder Patric Pirim . It would be relatively simple for a CMOS chip to implement in hardware the separate contributions of temporal and spatial processing in the brain. The brain-eye system uses layers of parallel-processing neurons that pass the signal through a series of preprocessing steps, resulting in real-time tracking of multiple moving objects within a visual scene.
Pirim created a chip architecture that mimicked the work of the neurons, with the help of multiplexing and memory. The result is an inexpensive device that can autonomously "perceive" and then track up to eight user-specified objects in a video stream based on hue, luminance, saturation, spatial orientation, speed and direction of motion.
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 shadow less 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 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 fault tolerant. By their design even if a group of neurons get, 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 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.
1 INTRODUCTION
While computing technology is growing in leaps and bounds, the human brain continues to be the world's fastest computer. Combine brain-power with seeing power, and you have the fastest, cheapest, most extra ordinary processor ever-the human eye. Little wonder, research labs the world over are striving to produce a near-perfect electronic eye.
The 'generic visual perception processor (GVPP)' has been developed after 10 long years of scientific effort. Generic Visual Perception Processor (GVPP) 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.
2 BACKGROUND OF THE INVENTION
The invention relates generally to methods and devices for automatic visual perception, and more particularly to methods and devices for processing image signals using two or more histogram calculation units to localize one or more objects in an image signal using one or more characteristics an object such as the shape, size and orientation of the object. Such devices can be termed an electronic spatio-temporal neuron, and are particularly useful for image processing, but may also be used for other signals, such as audio signals. The techniques of the present invention are also particularly useful for tracking one or more objects in real time.
It is desirable to provide devices including combined data processing units of a similar nature, each addressing a particular parameter extracted from the video signal. In particular, it is desirable to provide devices including multiple units for calculating histograms, or electronic spatio-temporal neuron STN, each processing a DATA (A), by a function in order to generate individually an output value.
The present invention also provides a method for perception of an object using characteristics, such as its shape, its size or its orientation, using a device composed of a set of histogram calculation units.
Using the techniques of the present invention, a general outline of a moving object is determined with respect to a relatively stable background, then inside this outline, elements that are characterized by their tone, color, relative position etc. are determined. .
3 POTIENTIAL SIGHTED
The GVPP was invented in 1992, by BEV founder Patric Pirim . It would be relatively simple for a CMOS chip to implement in hardware the separate contributions of temporal and spatial processing in the brain. The brain-eye system uses layers of parallel-processing neurons that pass the signal through a series of preprocessing steps, resulting in real-time tracking of multiple moving objects within a visual scene.
Pirim created a chip architecture that mimicked the work of the neurons, with the help of multiplexing and memory. The result is an inexpensive device that can autonomously "perceive" and then track up to eight user-specified objects in a video stream based on hue, luminance, saturation, spatial orientation, speed and direction of motion.
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 shadow less 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 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 fault tolerant. By their design even if a group of neurons get, 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 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.
