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      Indoor place categorization based on adaptive partitioning of texture histograms

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      ScienceOpen Research


      Image processing, Localization, Visual features, Spatial cognition, Place categorization

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          How can we localize ourselves within a building solely using visual information, i.e., when no data about prior location or movement are available? Here, we define place categorization as a set of three distinct image classification tasks for view matching, location matching, and room matching. We present a novel image descriptor built on texture statistics and dynamic image partitioning that can be used to solve all tested place classification tasks. We benchmark the descriptor by assessing performance of regularization on our own dataset as well as the established Indoor Environment under Changing conditionS dataset, which varies lighting condition, location, and viewing angle on photos taken within an office building. We show improvement on both the datasets against a number of baseline algorithms.

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          Most cited references 21

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          Simultaneous localization and mapping: part I

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            A feedforward architecture accounts for rapid categorization.

            Primates are remarkably good at recognizing objects. The level of performance of their visual system and its robustness to image degradations still surpasses the best computer vision systems despite decades of engineering effort. In particular, the high accuracy of primates in ultra rapid object categorization and rapid serial visual presentation tasks is remarkable. Given the number of processing stages involved and typical neural latencies, such rapid visual processing is likely to be mostly feedforward. Here we show that a specific implementation of a class of feedforward theories of object recognition (that extend the Hubel and Wiesel simple-to-complex cell hierarchy and account for many anatomical and physiological constraints) can predict the level and the pattern of performance achieved by humans on a rapid masked animal vs. non-animal categorization task.
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              Simultaneous localization and mapping (SLAM): part II


                Author and article information

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                ScienceOpen Research
                02 December 2014
                : 0 (ID: 5ed76ab3-2063-449e-8645-7066417ce021 )
                : 0
                : 1-7
                [1 ]Cognitive Neuroinformatics, University of Bremen, Bremen, Germany
                Author notes
                [* ]Corresponding author's e-mail address: sven2@ 123456uni-bremen.de
                © 2014 S. Eberhardt.

                This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

                Page count
                Figures: 4, Tables: 0, References: 36, Pages: 7
                Original article

                Image processing

                localization, spatial cognition, place categorization, image processing, vision


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