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      Automatic Museum Audio Guide

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          Abstract

          An automatic “museum audio guide” is presented as a new type of audio guide for museums. The device consists of a headset equipped with a camera that captures exhibit pictures and the eyes of things computer vision device (EoT). The EoT board is capable of recognizing artworks using features from accelerated segment test (FAST) keypoints and a random forest classifier, and is able to be used for an entire day without the need to recharge the batteries. In addition, an application logic has been implemented, which allows for a special highly-efficient behavior upon recognition of the painting. Two different use case scenarios have been implemented. The main testing was performed with a piloting phase in a real world museum. Results show that the system keeps its promises regarding its main benefit, which is simplicity of use and the user’s preference of the proposed system over traditional audioguides.

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          Random forest: a classification and regression tool for compound classification and QSAR modeling.

          Vladimir Svetnik, Andy Liaw, Christopher Tong … (2003)
          A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
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            A survey on wireless multimedia sensor networks

            Ian F. Akyildiz, Tommaso Melodia, Kaushik Chowdhury (2007)
            Article 0 comments Cited 170 times – based on 0 reviews     Review now
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              Faster and better: a machine learning approach to corner detection.

              Edward Rosten, Reid Porter, Tom Drummond (2009)
              The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.
              Article 0 comments Cited 74 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 31 January 2020
                Publication date Collection: February 2020
                Volume: 20
                Issue: 3
                Electronic Location Identifier: 779
                Affiliations
                [1 ]Visilab (Vision and Artificial Intelligence Group), University of Castilla-La Mancha (UCLM), E.T.S.I. Industrial, Avda Camilo Jose Cela s/n, 13071 Ciudad Real, Spain; JoseL.Espinosa@ 123456uclm.es
                [2 ]DFKI (Deutsches Forschungszentrum für Künstliche Intelligenz), Augmented Vision Research Group, Tripstaddterstr. 122, 67663 Kaiserslautern, Germany; stephan.krauss@ 123456dfki.de (S.K.); alain.pagani@ 123456dfki.de (A.P.)
                [3 ]Fluxguide, Burggasse 7-9/9, 1070 Vienna, Austria; kasra@ 123456fluxguide.com
                Author notes
                Author information
                https://orcid.org/0000-0002-5092-8275
                https://orcid.org/0000-0001-5377-848X
                https://orcid.org/0000-0002-0841-4131
                Article
                Publisher ID: sensors-20-00779
                DOI: 10.3390/s20030779
                PMC ID: 7038402
                PubMed ID: 32023954
                SO-VID: 4798a671-4ada-464f-a9e0-a7c781c65740
                Copyright © © 2020 by the authors.
                License:

                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
                Date received : 22 November 2019
                Date accepted : 29 January 2020
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: internet of things (iot),computer vision,automatic audioguide,artificial intelligence,systems on chip (soc)
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: internet of things (iot), computer vision, automatic audioguide, artificial intelligence, systems on chip (soc)

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