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Pattern Recognition. ICPR International Workshops and Challenges

Virtual Event, January 10–15, 2021, Proceedings, Part VI

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Alberto Del Bimbo

    1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

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  2. Rita Cucchiara

    1. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

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  3. Stan Sclaroff

    1. Department of Computer Science, Boston University, Boston, USA

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  4. Giovanni Maria Farinella

    1. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

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  5. Tao Mei

    1. Cloud & AI, JD.COM, Beijing, China

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  6. Marco Bertini

    1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

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  7. Hugo Jair Escalante

    1. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Mexico

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  8. Roberto Vezzani

    1. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12666)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: ICPR 2021.

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About this book

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.

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Keywords

Table of contents (60 papers)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. MAES - Machine Learning Advances Environmental Science

    1. Front Matter

      Pages 1-4
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    2. Finding Relevant Flood Images on Twitter Using Content-Based Filters

      • Björn Barz, Kai Schröter, Ann-Christin Kra, Joachim Denzler
      Pages 5-14

    3. Natural Disaster Classification Using Aerial Photography Explainable for Typhoon Damaged Feature

      • Takato Yasuno, Masazumi Amakata, Masahiro Okano
      Pages 15-25

    4. Environmental Time Series Prediction with Missing Data by Machine Learning and Dynamics Recostruction

      • Francesco Camastra, Vincenzo Capone, Angelo Ciaramella, Tony Christian Landi, Angelo Riccio, Antonino Staiano
      Pages 26-33

    5. Semi-Supervised Learning for Grain Size Distribution Interpolation

      • Konstantin Kobs, Christian Schäfer, Michael Steininger, Anna Krause, Roland Baumhauer, Heiko Paeth et al.
      Pages 34-44

    6. Location-Specific vs Location-Agnostic Machine Learning Metamodels for Predicting Pasture Nitrogen Response Rate

      • Christos Pylianidis, Val Snow, Dean Holzworth, Jeremy Bryant, Ioannis N. Athanasiadis
      Pages 45-54

    7. Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden

      • Giovanni Giacalone, Giosué Lo Bosco, Marco Barra, Angelo Bonanno, Giuseppa Buscaino, Riko Noormets et al.
      Pages 55-64

    8. Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)

      • Ignazio Fontana, Giovanni Giacalone, Riccardo Rizzo, Marco Barra, Olga Mangoni, Angelo Bonanno et al.
      Pages 65-74

    9. Multi-Input ConvLSTM for Flood Extent Prediction

      • Leo Muckley, James Garforth
      Pages 75-85

    10. Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams

      • Ji Yeon Baek, Maria Krishna de Guzman, Ho-min Park, Sanghyeon Park, Boyeon Shin, Tanja Cirkovic Velickovic et al.
      Pages 86-97

    11. A Machine Learning Approach to Chlorophyll a Time Series Analysis in the Mediterranean Sea

      • F. Mattei, M. Scardi
      Pages 98-109

    12. Plankton Recognition in Images with Varying Size

      • Jaroslav Bureš, Tuomas Eerola, Lasse Lensu, Heikki Kälviäinen, Pavel Zemčík
      Pages 110-120

    13. Environment Object Detection for Marine ARGO Drone by Deep Learning

      • Angelo Ciaramella, Francesco Perrotta, Gerardo Pappone, Pietro Aucelli, Francesco Peluso, Gaia Mattei
      Pages 121-129

    14. Unsupervised Learning of High Dimensional Environmental Data Using Local Fractality Concept

      • Mikhail Kanevski, Mohamed Laib
      Pages 130-138

    15. Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network

      • Jelle Hofman, Tien Huu Do, Xuening Qin, Esther Rodrigo, Martha E. Nikolaou, Wilfried Philips et al.
      Pages 139-147

    16. How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?

      • Benjamin Deneu, Alexis Joly, Pierre Bonnet, Maximilien Servajean, François Munoz
      Pages 148-158

  3. ManifLearn - Manifold Learning in Machine Learning, from Euclid to Riemann

    1. Front Matter

      Pages 159-161
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    2. Latent Space Geometric Statistics

      • Line Kühnel, Tom Fletcher, Sarang Joshi, Stefan Sommer
      Pages 163-178

    3. Improving Neural Network Robustness Through Neighborhood Preserving Layers

      • Bingyuan Liu, Christopher Malon, Lingzhou Xue, Erik Kruus
      Pages 179-195
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Other volumes

  1. Pattern Recognition. ICPR International Workshops and Challenges

  2. Pattern Recognition. ICPR International Workshops and Challenges

  3. Pattern Recognition. ICPR International Workshops and Challenges

  4. Pattern Recognition. ICPR International Workshops and Challenges

  5. Pattern Recognition. ICPR International Workshops and Challenges

  6. Pattern Recognition. ICPR International Workshops and Challenges

  7. Pattern Recognition. ICPR International Workshops and Challenges

  8. Pattern Recognition. ICPR International Workshops and Challenges

Editors and Affiliations

  • Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo, Marco Bertini

  • Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara, Roberto Vezzani

  • Department of Computer Science, Boston University, Boston, USA

    Stan Sclaroff

  • Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  • Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  • Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Mexico

    Hugo Jair Escalante

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