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      Revisiting Active Perception

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          Abstract

          Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.

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          Efficient Graph-Based Image Segmentation

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            Tactile Sensing—From Humans to Humanoids

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              State-of-the-art in visual attention modeling.

              Modeling visual attention--particularly stimulus-driven, saliency-based attention--has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.
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                Author and article information

                Journal
                2016-03-08
                2016-03-13
                Article
                1603.02729
                f6d355cb-458d-438a-8597-b29e338736ee

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.CV cs.RO

                Computer vision & Pattern recognition,Robotics
                Computer vision & Pattern recognition, Robotics

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