TP-LSD: Tri-Points Based Line Segment Detector
This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed. The previous related methods typically use the two-step strategy, relying on either heuristic po
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Andrea Vedaldi Horst Bischof Thomas Brox Jan-Michael Frahm (Eds.)
Computer Vision – ECCV 2020 16th European Conference Glasgow, UK, August 23–28, 2020 Proceedings, Part XXVII
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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Andrea Vedaldi Horst Bischof Thomas Brox Jan-Michael Frahm (Eds.) •
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Computer Vision – ECCV 2020 16th European Conference Glasgow, UK, August 23–28, 2020 Proceedings, Part XXVII
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Editors Andrea Vedaldi University of Oxford Oxford, UK
Horst Bischof Graz University of Technology Graz, Austria
Thomas Brox University of Freiburg Freiburg im Breisgau, Germany
Jan-Michael Frahm University of North Carolina at Chapel Hill Chapel Hill, NC, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-58582-2 ISBN 978-3-030-58583-9 (eBook) https://doi.org/10.1007/978-3-030-58583-9 LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword
Hosting the European Conference on Computer Vision (ECCV 2020) was certainly an exciting journey. From the 2016 plan to hold it at the Edinburgh International Conference Centre (hosting 1,800 de
