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

1. Front Matter

   Pages i-xxx

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2. Image Denoising

   1. Front Matter

      Pages 1-1

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   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

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   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

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   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

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. 

• artificial intelligence
• classification
• clustering
• computational linguistics
• computer networks
• Human-Computer Interaction (HCI)
• image processing
• image reconstruction
• image segmentation
• imaging systems
• learning algorithms
• machine learning
• neural networks
• recurrent neural networks
• robotics
• semantics
• sensors
• signal processing
• Support Vector Machines (SVM)
• user interfaces

• 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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