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We propose a novel approach to self-sensing spaces, in which classical computer vision algorithms are empowered by opportunities presented by the pervasive space. Our approach, which we call PerVision, extends classical object recognition and tracking algorithms by adding a self-assessment/adjustment loop in which sensors and actuators of the pervasive space are used to vary scene parameters to minimize errors in the recognition process. We present the PerVision concept and algorithms in the context of locating and tracking dumb objects such as furniture in a smart house.
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