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Visual sensor networks (VSNs) have attracted the interest of researchers worldwide in the last few years, and are expected to play a major role in the evolution of the Internet-of-Things (IoT). When used to perform visual analysis tasks, VSNs may be operated according to two different paradigms. In the traditional compress-then-analyze paradigm, images are acquired, compressed and transmitted for...
This work focuses on Visual Sensor Networks (VSNs) which perform visual analysis tasks such as object recognition. There, the goal is to find the image in a reference database which is the closest match to the image captured by camera sensor nodes. Recognition is performed by relying on visual features extracted from the acquired image, which are matched against a database of labeled features in order...
Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectures have been proposed, in order to enable image analysis on energy-constrained devices such as smart-phones or Visual Sensor Networks (VSN). In this work, we dissect and analyze BRISK,...
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