Boots – shoes – and leggings
Patent
1996-11-25
1998-12-15
Mai, Tan V.
Boots, shoes, and leggings
364602, G06G 702
Patent
active
058503579
DESCRIPTION:
BRIEF SUMMARY
FIELD OF THE INVENTION
The present invention relates generally to signal processing, and more specifically to apparatus for carrying out the equalization and enhancement of audio and video signals via a dynamic range reduction which is performed by a non-causal feedback automatic gain control (AGC) algorithm.
BACKGROUND OF THE INVENTION
The dynamic range (DR) of a signal is determined by the ratio between the maximum value that the signal may possess, and the minimum significant difference between any pair of samples within the signal. Hence, the signal DR defines how many distinct levels of intensity a subsequent user of the signal must have, in order to fully exploit the information conveyed by the signal. The DR is a major factor in the cost of processors, data storages, and data communication links. The DR of media and displays such as loudspeakers, electronic picture-tubes and picture-paper is physically limited. Typical limitation of visual media is 256 distinct levels, while some state of the art sensors have output signal DR that approach 10.sup.5. Many details of meaningful information of such high DR signals will be lost when displayed on a picture-tube or on paper.
The DR of light intensities in natural views and scenarios may exceed 10.sup.10. It has been known for a long time that the eyes of animals, as well as the human eye, are capable of reducing the DR of detected light signals by many orders of magnitudes. Some signal processing performed in biological sensors such as the eye, have been successfully modeled by a spatial, or non-causal feedback AGC algorithm, whose block diagram is schematically given in FIG. 1. In this model the input signal is multiplied by a difference of a constant and a non-causal average of the output signal. As a result, the transmission of this model to the average level of the input signal exhibits a logarithmic nature, according to curve A of FIG. 2. Due to the average logarithmic transmission, the average level of the output signal cannot exceed a prescribed constant value, despite an arbitrary increase in the input average level. Simultaneously, The transmission of the same model has the trend to enhance local variations in the input signal, according to curves B of FIG. 2. The combined result of these two different responses of the said AGC model is a decrease of the signal dynamic range which is obtained without any apparent loss of information. This double-role operation is termed "Equalization-Enhancement".
THE PRIOR ART
The generalized approximation of the said model constitutes the class of neighborhood transforms which actually cover many known signal processing techniques, such as convolution. Signal processing generally consists of three phases: preprocessing, data reduction, and recognition. At the preprocessing phase, the complete signal is usually processed by algorithms which are unaware of the content of the signal, and merely improve its quality. Examples are histogram equalization, noise reduction, peak detection, smoothing, thresholding, etc.
The preprocessing phase of signal processing requires the handling of a huge flow of information. Many preprocessing algorithms operate on small neighborhoods or segments of the signal at a time, while others, such as histogram processing and certain integral transforms, process the whole signal before any result can be generated. Some preprocessing systems are designed to imitate the human eye, but are merely approximations of what the eye can do.
Some conventional image processing systems acquire images from an image acquisition unit, such as a camera, which produces each image in a raster scan format. The video signal is typically forwarded to a pipeline of neighborhood processors, such as convolvers, and integral processors such as histogram and fast Fourier transformers. In addition, the preprocessors typically generate their outputs in a raster scan format so that one preprocessor can directly feed into the next one. The approximate AGC algorithm in image processing is a special case of neigh
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Grant et al, "Recent Advances in Analog Signal Processing" IEEE Trans. on Aerospace and Electronic Sys., vol. 26, No. 5 pp. 818-849, Sep., 1, 1990.
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