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Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Igor V. Tetko

    1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

  2. Věra Kůrková

    1. Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

  3. Pavel Karpov

    1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

  4. Fabian Theis

    1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11729)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ICANN 2019.

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About this book

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. 

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Table of contents (57 papers)

  1. Front Matter

    Pages i-xxx
    Download chapter PDF

  2. Image Denoising

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification

      • Jose Carranza-Rojas, Saul Calderon-Ramirez, Adán Mora-Fallas, Michael Granados-Menani, Jordina Torrents-Barrena
      Pages 3-16

    3. Distortion Estimation Through Explicit Modeling of the Refractive Surface

      • Szabolcs Pável, Csanád Sándor, Lehel Csató
      Pages 17-28

    4. Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation

      • Cheng Peng, Qing Xu, Yuejun Guo, Klaus Schoeffmann
      Pages 29-40

    5. IBDNet: Lightweight Network for On-orbit Image Blind Denoising

      • Ling Li, Junxing Hu, Yijun Lin, Fengge Wu, Junsuo Zhao
      Pages 41-52

  3. Object Detection

    1. Front Matter

      Pages 53-53
      Download chapter PDF

    2. Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification

      • Tingyu Wei, Wenxin Hu, Xingjiao Wu, Yingbin Zheng, Hao Ye, Jing Yang et al.
      Pages 55-67

    3. Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropout

      • Tomoyuki Myojin, Shintaro Hashimoto, Kenji Mori, Keisuke Sugawara, Naoki Ishihama
      Pages 68-80

    4. An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key

      • Bartosz Czaplewski
      Pages 81-92

    5. A New Learning-Based One Shot Detection Framework for Natural Images

      • Sen Na, Ruoyu Yan
      Pages 93-104

    6. Dense Receptive Field Network: A Backbone Network for Object Detection

      • Fei Gao, Chengguang Yang, Yisu Ge, Shufang Lu, Qike Shao
      Pages 105-118

    7. Referring Expression Comprehension via Co-attention and Visual Context

      • Youming Gao, Yi Ji, Ting Xu, Yunlong Xu, Chunping Liu
      Pages 119-130

    8. Comparison Between U-Net and U-ReNet Models in OCR Tasks

      • Brian B. Moser, Federico Raue, Jörn Hees, Andreas Dengel
      Pages 131-142

    9. Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation

      • Ming Yuan, Zhilei Chai, Wenlai Zhao
      Pages 143-156

    10. Action Recognition Based on Divide-and-Conquer

      • Guanghua Tan, Rui Miao, Yi Xiao
      Pages 157-167

    11. An Adaptive Feature Channel Weighting Scheme for Correlation Tracking

      • Zhen Zhang, Chao Wang, Xiqun Lu
      Pages 168-183

    12. In-Silico Staining from Bright-Field and Fluorescent Images Using Deep Learning

      • Dominik Jens Elias Waibel, Ulf Tiemann, Valerio Lupperger, Henrik Semb, Carsten Marr
      Pages 184-186

  4. Image Segmentation

    1. Front Matter

      Pages 187-187
      Download chapter PDF

    2. A Lightweight Neural Network for Hard Exudate Segmentation of Fundus Image

      • Song Guo, Tao Li, Kai Wang, Chan Zhang, Hong Kang
      Pages 189-199
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Other volumes

  1. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation

  2. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

  3. Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

  4. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series

  5. Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions

Editors and Affiliations

  • Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko, Pavel Karpov, Fabian Theis

  • Institute of Computer Science, Czech Academy of Sciences, Praha 8, Czech Republic

    Věra Kůrková

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