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      Efficient 3D Objects Recognition Using Multifoveated Point Clouds

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          Abstract

          Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that remains is the demand for computationally intensive processing that required optimized approaches to deal with 3D vision modeling, especially when it is necessary to perform tasks in real time. A previously proposed multi-resolution 3D model known as foveated point clouds can be a possible solution to this problem. Nevertheless, this is a model limited to a single foveated structure with context dependent mobility. In this work, we propose a new solution for data reduction and feature detection using multifoveation in the point cloud. Nonetheless, the application of several foveated structures results in a considerable increase of processing since there are intersections between regions of distinct structures, which are processed multiple times. Towards solving this problem, the current proposal brings an approach that avoids the processing of redundant regions, which results in even more reduced processing time. Such approach can be used to identify objects in 3D point clouds, one of the key tasks for real-time applications as robotics vision, with efficient synchronization allowing the validation of the model and verification of its applicability in the context of computer vision. Experimental results demonstrate a performance gain of at least 27.21% in processing time while retaining the main features of the original, and maintaining the recognition quality rate in comparison with state-of-the-art 3D object recognition methods.

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          Most cited references51

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          Visual Place Recognition: A Survey

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            Bottom-up and top-down attention: different processes and overlapping neural systems.

            The brain is limited in its capacity to process all sensory stimuli present in the physical world at any point in time and relies instead on the cognitive process of attention to focus neural resources according to the contingencies of the moment. Attention can be categorized into two distinct functions: bottom-up attention, referring to attentional guidance purely by externally driven factors to stimuli that are salient because of their inherent properties relative to the background; and top-down attention, referring to internal guidance of attention based on prior knowledge, willful plans, and current goals. Over the past few years, insights on the neural circuits and mechanisms of bottom-up and top-down attention have been gained through neurophysiological experiments. Attention affects the mean neuronal firing rate as well as its variability and correlation across neurons. Although distinct processes mediate the guidance of attention based on bottom-up and top-down factors, a common neural apparatus, the frontoparietal network, is essential in both types of attentional processes. © The Author(s) 2013.
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              3-D Mapping With an RGB-D Camera

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                16 July 2018
                July 2018
                : 18
                : 7
                : 2302
                Affiliations
                [1 ]Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN 59.078-970, Brazil; fabio.veritate@ 123456gmail.com (F.F.O.); mfernandes@ 123456dca.ufrn.br (M.A.C.F.); rafaelbg@ 123456dimap.ufrn.br (R.B.G.)
                [2 ]Department of Computer Science, State University of Rio Grande do Norte, Natal, RN, 59104-200 Brazil; and.abner@ 123456gmail.com
                Author notes
                [* ]Correspondence: lmarcos@ 123456dca.ufrn.br ; Tel.: +55-84-3215-3771
                [†]

                Current address: DCA-CT-UFRN, Campus Universitario, Lagoa Nova, Natal, RN 59.078-970, Brazil.

                Author information
                https://orcid.org/0000-0003-3354-404X
                https://orcid.org/0000-0001-6353-8674
                https://orcid.org/0000-0001-7536-2506
                https://orcid.org/0000-0002-9390-912X
                https://orcid.org/0000-0002-7735-5630
                Article
                sensors-18-02302
                10.3390/s18072302
                6068497
                30012990
                40022260-87b5-4e04-b29b-e8320042db87
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 June 2018
                : 11 July 2018
                Categories
                Article

                Biomedical engineering
                multifoveated structure,3d object recognition,point clouds
                Biomedical engineering
                multifoveated structure, 3d object recognition, point clouds

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