1 April 1991 Architecture for surveillance in real time using nonlinear image-processing hardware
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Abstract
A remote machine vision system is presented which addresses three critical aspects of surveillance and vision in general. First, to deal successfully with changing weather conditions and fast events in real time. Second, the false alarm rate must be very low since the system may operate 24 hours a day all year round. Third, to send out visual information to the head of security immediately, wherever he may be. This visual information consists of the track the intruder left and its silhouette. This allows the official to distinguish between human and nonhuman intruders. The key to this architecture is an arithmetical subtraction which is done pixel-by-pixel over the whole image. Basically, it is a difference between a reference image (clean image) and the one which is being received. Other steps of the process are multiple threshold and low-pass filtering. Filtering and dynamic range splitting are the domains in which we have worked using digital hardware techniques. Very consistent results were obtained by adaptive mean filtering and 3-class splitting respectively. Considerable progress is being made in developing an adaptive n-class splitting. Special relevance has been given to the imaging hardware which is able to control, acquire, digitize, filter, compare, and add images and transmit them over a telephone line with appropriate alarms and display.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cassiano Paixao Pais, Cassiano Paixao Pais, Fernando D. Carvalho, Fernando D. Carvalho, Victor M. Silvestre, Victor M. Silvestre, } "Architecture for surveillance in real time using nonlinear image-processing hardware", Proc. SPIE 1451, Nonlinear Image Processing II, (1 April 1991); doi: 10.1117/12.44334; https://doi.org/10.1117/12.44334
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