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

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

Table of Contents

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    Book Overview
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    Chapter 1 Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification
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    Chapter 2 Distortion Estimation Through Explicit Modeling of the Refractive Surface
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    Chapter 3 Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation
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    Chapter 4 IBDNet: Lightweight Network for On-orbit Image Blind Denoising
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    Chapter 5 Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification
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    Chapter 6 Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropout
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    Chapter 7 An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key
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    Chapter 8 A New Learning-Based One Shot Detection Framework for Natural Images
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    Chapter 9 Dense Receptive Field Network: A Backbone Network for Object Detection
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    Chapter 10 Referring Expression Comprehension via Co-attention and Visual Context
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    Chapter 11 Comparison Between U-Net and U-ReNet Models in OCR Tasks
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    Chapter 12 Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation
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    Chapter 13 Action Recognition Based on Divide-and-Conquer
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    Chapter 14 An Adaptive Feature Channel Weighting Scheme for Correlation Tracking
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    Chapter 15 In-Silico Staining from Bright-Field and Fluorescent Images Using Deep Learning
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    Chapter 16 A Lightweight Neural Network for Hard Exudate Segmentation of Fundus Image
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    Chapter 17 Attentional Residual Dense Factorized Network for Real-Time Semantic Segmentation
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    Chapter 18 Random Drop Loss for Tiny Object Segmentation: Application to Lesion Segmentation in Fundus Images
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    Chapter 19 Flow2Seg: Motion-Aided Semantic Segmentation
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    Chapter 20 COCO_TS Dataset: Pixel–Level Annotations Based on Weak Supervision for Scene Text Segmentation
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    Chapter 21 Learning Deep Structured Multi-scale Features for Crisp and Object Occlusion Edge Detection
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    Chapter 22 Graph-Boosted Attentive Network for Semantic Body Parsing
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    Chapter 23 A Global-Local Architecture Constrained by Multiple Attributes for Person Re-identification
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    Chapter 24 Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders
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    Chapter 25 Learning Relational-Structural Networks for Robust Face Alignment
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    Chapter 26 An Efficient 3D-NAS Method for Video-Based Gesture Recognition
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    Chapter 27 Robustness of Deep LSTM Networks in Freehand Gesture Recognition
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    Chapter 28 Delving into the Impact of Saliency Detector: A GeminiNet for Accurate Saliency Detection
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    Chapter 29 FCN Salient Object Detection Using Region Cropping
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    Chapter 30 Object-Level Salience Detection by Progressively Enhanced Network
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    Chapter 31 Action Unit Assisted Facial Expression Recognition
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    Chapter 32 Discriminative Feature Learning Using Two-Stage Training Strategy for Facial Expression Recognition
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    Chapter 33 Action Units Classification Using ClusWiSARD
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    Chapter 34 Automatic Estimation of Dog Age: The DogAge Dataset and Challenge
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    Chapter 35 Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
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    Chapter 36 Variational Deep Embedding with Regularized Student-t Mixture Model
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    Chapter 37 A Mixture-of-Experts Model for Vehicle Prediction Using an Online Learning Approach
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    Chapter 38 Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks
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    Chapter 39 On the Inability of Markov Models to Capture Criticality in Human Mobility
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    Chapter 40 LSTM with Uniqueness Attention for Human Activity Recognition
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    Chapter 41 Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training
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    Chapter 42 Generative Creativity: Adversarial Learning for Bionic Design
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    Chapter 43 Self-attention StarGAN for Multi-domain Image-to-Image Translation
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    Chapter 44 Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic
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    Chapter 45 Constraint-Based Visual Generation
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    Chapter 46 Text to Image Synthesis Based on Multiple Discrimination
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    Chapter 47 Disentangling Latent Factors of Variational Auto-encoder with Whitening
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    Chapter 48 Training Discriminative Models to Evaluate Generative Ones
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    Chapter 49 Scene Graph Generation via Convolutional Message Passing and Class-Aware Memory Embeddings
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    Chapter 50 Change Detection in Satellite Images Using Reconstruction Errors of Joint Autoencoders
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    Chapter 51 Physical Adversarial Attacks by Projecting Perturbations
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    Chapter 52 Improved Forward-Backward Propagation to Generate Adversarial Examples
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    Chapter 53 Incremental Learning of GAN for Detecting Multiple Adversarial Attacks
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    Chapter 54 Evaluating Defensive Distillation for Defending Text Processing Neural Networks Against Adversarial Examples
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    Chapter 55 DCT: Differential Combination Testing of Deep Learning Systems
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    Chapter 56 Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection
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    Chapter 57 HLR: Generating Adversarial Examples by High-Level Representations
Attention for Chapter 38: Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks
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Chapter title
Analysis of Dogs’ Sleep Patterns Using Convolutional Neural Networks
Chapter number 38
Book title
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing
Published by
Springer, Cham, September 2019
DOI 10.1007/978-3-030-30508-6_38
Book ISBNs
978-3-03-030507-9, 978-3-03-030508-6

Anna Zamansky, Aleksandr M. Sinitca, Dmitry I. Kaplun, Michael Plazner, Ivana G. Schork, Robert J. Young, Cristiano S. de Azevedo, Zamansky, A, Sinitca, AM, Kaplun, DI, Plazner, M, Schork, IG, Young, RJ, de Azevedo, CS, Zamansky, Anna, Sinitca, Aleksandr M., Kaplun, Dmitry I., Plazner, Michael, Schork, Ivana G., Young, Robert J., de Azevedo, Cristiano S.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 43%
Student > Ph. D. Student 3 11%
Student > Bachelor 2 7%
Professor > Associate Professor 2 7%
Student > Doctoral Student 1 4%
Other 5 18%
Unknown 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 36%
Computer Science 4 14%
Veterinary Science and Veterinary Medicine 3 11%
Engineering 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 3 11%
Unknown 5 18%