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- W87887430 abstract "An approach for the semantic interpretation of image-based novelty in real-world environments is presented. We measure novelty using the concept of pixel-based surprise, which quantifies how much a new observation changes the robot's current probabilistic appearance model of the environment. The corresponding surprise maps are utilized as prior information to reduce the search space of a Histograms of Oriented Gradients object detector. Specifically, detection windows are scored and selected using surprise values. Several object classes are simultaneously searched for and learned from a low number of manually taken reference images. Experiments are performed on a human-size robot in a cluttered household environment. Compared to object detection based on a search of the complete image, a 35-fold speed-up is observed. Additionally, the detection performance increases significantly." @default.
- W87887430 created "2016-06-24" @default.
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- W87887430 date "2012-01-01" @default.
- W87887430 modified "2023-09-26" @default.
- W87887430 title "Semantic Interpretation of Novelty in Images Using Histograms of Oriented Gradients" @default.
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- W87887430 doi "https://doi.org/10.1007/978-3-642-33503-7_42" @default.
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