Cesar Bandera

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peripheral vision captures low frequency cues in the scene without gross A new class of machine vision systems is proposed, called foveal vision systems. These systems, modeled after advanced biological vision. feature space-variant (variable resolution) imager topologies and a c l d l o o p system architecture. The imager topology is characterized by high(More)
Foveal vision features imagers with graded acuity coupled with context sensitive sensor gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision operates more efficiently than uniform acuity vision because resolution is treated as a dynamically allocatable resource, but requires a more refined visual attention mechanism. We(More)
This paper presents a method for detecting and classifying a target from its foveal (graded resolution) imagery using a multiresolution neural network. Target identification decisions are based on minimizing an energy function. This energy function is evaluated by comparing a candidate blob with a library of target models at several levels of resolution(More)
High network connectivity and low energy consumption are two major challenges in wireless sensor networks (WSNs). It is even more challenging to achieve both at the same time. To tackle the problem, this paper proposes a novel disjoint Set Division (SEDO) algorithm for joint scheduling and routing in WSNs. We finely divide sensors into different disjoint(More)
This paper proposes a method for identifying and classifying a target from its foveal imagery using a neural network. The method’s criterion for identifying a target is based on finding the global minimum of an energy function. This energy function is characterized by matching the candidate target and a library of target models at several levels of(More)