Lecture Notes in Computer Science 12594 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 More information about this subseries at http://www.springer.com/series/7412 Maria De Marsico • Gabriella Sanniti di Baja • Ana Fred (Eds.) Pattern Recognition Applications and Methods 9th International Conference, ICPRAM 2020 Valletta, Malta, February 22–24, 2020 Revised Selected Papers 123 Editors Maria De Marsico Gabriella Sanniti di Baja Sapienza Università di Roma ICAR Roma, Italy Consiglio Nazionale delle Ricerche Naples, Napoli, Italy Ana Fred Instituto de Telecomunicações Lisbon, Portugal University of Lisbon Lisbon, Portugal ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-66124-3 ISBN 978-3-030-66125-0 (eBook) https://doi.org/10.1007/978-3-030-66125-0 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 Preface This book includes the extended and revised versions of a set of selected papers from the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), held in Valletta, Malta, during February 22–24, 2020. ICPRAM is a major point of contact between researchers, engineers, and practi- tioners on the areas of pattern recognition and machine learning, both from theoretical and application perspectives. Contributions describing applications of pattern recog- nition techniques to real-world problems, interdisciplinary research, experimental, and/or theoretical studies yielding new insights that advance pattern recognition methods are especially encouraged. ICPRAM 2020 received 102 paper submissions from 33 countries, of which the seven papers in this book constitute the 7%. The seven papers were selected by the event chairs and their selection was based on a number of criteria that include the classifications and comments provided by the Program Committee members, the ses- sion chairs’ assessment, and also the program chairs’ global view of all papers included in the technical program. The authors of the selected papers were then invited to submit a revised and extended version of their papers, having at least 30% innovative material. The first four papers in the book are in the applications area, while the remaining three papers are in the theory and methods area. In the first paper, the authors face the problem of building a universal classifier for traffic sign recognition. The classifier has to deal with large intra-class variations in the classes and also similarities among various sign classes. Authors use attention network for country independent classification. The new building block architecture shows significant improvement of classification accuracy with respect to building block architecture (VGG) used in a previous paper. In the second paper, multi-object tracking and segmentation (MOTS) of moving objects in traffic video datasets are considered. A novel method for tracking multiple objects (MaskADNet) is proposed, which uses masked images as input for training ADNet. The segmentation masks obtained after tracking using MaskADNet have a better Jaccard index or Intersection over Union for masks. The third paper deals with emotion recognition (ER). Authors discuss methods for analyzing the non-linguistic component of vocalized speech and propose a method for producing lower dimensional representations of sound spectrograms that respect their temporal structure. By taking into account that most modern day consumer cameras are affected by some level of radial distortion, which must be compensated for in order to get accurate estimates, authors of the fourth paper propose a novel polynomial solver for radially distorted planar motion compatible homographies. The suggested algorithm is shown to work well inside a RANSAC loop on both synthetic and real data. In the fifth paper, data acquired in a natural mixed forest by means of an unmanned aerial vehicle are considered. A suitable pre-processing step is introduced after which vi Preface six common clustering algorithms are used to detect tree tops and five different deep learning architectures are employed to classify tree tops depending on the degree of affectation due to a parasite infestation. Classification results reach error rates as low as 0.096. The sixth paper deals with deep neural networks (DNNs) and investigates how to reduce model complexity – without performance degradation – by pruning useless connections. Authors try to answer the question of “how similar are representations in pruned and unpruned models?” and show that the results depend critically on the used similarity measure. Finally, in the last paper, the authors analyze multiple approaches in indefinite learning and suggest a novel, efficient preprocessing operation which widely preserves the domain-specific information, while still providing a Mercer kernel function. In particular, we address practical aspects like a out of sample extension and an effective implementation of the approach. An extensive experimental work is done on various typical data sets obtaining superior results in the field. We would like to thank all the authors for their contributions and also the reviewers who helped ensure the quality of this publication. February 2020 Maria De Marsico Gabriella Sanniti di Baja Ana Fred Organization Conference Chair Ana Fred Instituto de Telecomunicações and University of Lisbon, Portugal Program Co-chairs Maria De Marsico Sapienza Università di Roma, Italy Gabriella Sanniti di Baja CNR, Italy Program Committee Andrea Abate University of Salerno, Italy Ashraf AbdelRaouf Misr International University (MIU), Egypt Rahib Abiyev Near East University, Turkey Lale Akarun Bogazici University, Turkey Mayer Aladjem Ben-Gurion University of the Negev, Israel Rocío Alaiz-Rodríguez Universidad de León, Spain Mahmood Azimi-Sadjadi Colorado State University, USA Silvio Barra University of Cagliari, Italy Stefano Berretti University of Florence, Italy Monica Bianchini University of Siena, Italy Isabelle Bloch Télécom ParisTech, Université Paris-Saclay, France Andrea Bottino Politecnico di Torino, Italy Paula Brito Universidade do Porto, Portugal Fabian Bürger Valeo Vision, France Marinella Cadoni Università degli Studi di Sassari, Italy Javier Calpe Universitat de València, Spain Virginio Cantoni Università degli Studi di Pavia, Italy Guillaume Caron Université de Picardie Jules Verne, France Rama Chellappa University of Maryland, USA Sergio Cruces Universidad de Sevilla, Spain Rozenn Dahyot Trinity College Dublin, Ireland Luiza de Macedo Mourelle State University of Rio de Janeiro, Brazil Yago Diez Yamagata University, Japan Jean-Louis Dillenseger Université de Rennes 1, France Duane Edgington Monterey Bay Aquarium Research Institue, USA Kjersti Engan University of Stavanger, Norway Haluk Eren Firat University, Turkey Giorgio Fumera University of Cagliari, Italy Vicente Garcia Autonomous University of Ciudad Juárez, Mexico viii Organization Angelo Genovese Università degli Studi di Milano, Italy Markus Goldstein Ulm University of Applied Sciences, Germany Petra Gomez-Krämer La Rochelle University, France Rocio Gonzalez-Diaz University of Seville, Spain Bernard Gosselin University of Mons, Belgium Marco Granato Università degli Studi di Milano, Italy Michal Haindl Institute of Information Theory and Automation, Czech Republic Lawrence Hall University of South Florida, USA Kouichi Hirata Kyushu Institute of Technology, Japan Sean Holden University of Cambridge, UK Yi-zeng Hsieh National Taiwan Ocean University, Taiwan, China Su-Yun Huang Academia Sinica, Taiwan, China Akinori Ito Tohoku University, Japan Yuji Iwahori Chubu University, Japan Sarangapani Jagannathan Missouri University of Science and Technology, USA Xiaoyi Jiang University of