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    Indoor place categorization based on adaptive partitioning of texture histograms

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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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        Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

        A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.
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          A systematic analysis of performance measures for classification tasks

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            Simultaneous localization and mapping: part I


              Author and article information

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

              Figures: 4, Tables: 0, References: 36, Pages: 7
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