| Description |
1 online resource |
| Series |
Lecture Notes in Artificial Intelligence |
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Lecture notes in computer science ; 12294 |
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LNCS sublibrary: SL 7, Artificial intelligence |
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Lecture notes in computer science. Lecture notes in artificial intelligence.
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Lecture notes in computer science ; 12294.
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LNCS sublibrary. SL 7, Artificial intelligence.
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| Contents |
Intro -- Preface -- Organization -- Contents -- Invited Papers -- Deep Learning Methods for Image Guidance in Radiation Therapy -- 1 Introduction -- 2 Motion Monitoring During Treatment -- 2.1 Tracking of Bony Structures -- 2.2 Soft Tissue Tracking -- 3 CBCT Image Reconstruction -- 3.1 X-ray Projection Pre-processing -- 3.2 CBCT Volume Post-processing -- 3.3 Iterative CBCT Reconstruction Methods -- 3.4 End-to-End CBCT Image Reconstruction Learning -- 3.5 4D CBCT Reconstruction -- 4 Deep Learning for Organ Segmentation -- 5 Deformable Image Registration -- 6 Conclusion -- References |
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Intentional Image Similarity Search -- 1 Introduction -- 2 Related Work -- 3 German Broadcasting Archive -- 4 A Novel Approach to Intentional Image Similarity Search -- 4.1 Query Specification -- 4.2 Hybrid Feature Method -- 4.3 Plugin Mechanism -- 5 Experimental Results -- 6 Conclusion -- References -- Foundations -- Structured (De)composable Representations Trained with Neural Networks -- 1 Introduction -- 2 Background -- 3 CoDiR: Method -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 5 Conclusion -- References |
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Long Distance Relationships Without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling -- 1 Introduction -- 1.1 Motivation -- 2 Method -- 2.1 Original RSM Model -- 2.2 Boosted RSM (bRSM) -- 3 Experiments -- 3.1 Stochastic Sequential MNIST (ssMNIST) -- 3.2 Language Modeling -- 4 Conclusion -- References -- Improving Accuracy and Efficiency of Object Detection Algorithms Using Multiscale Feature Aggregation Plugins -- 1 Introduction -- 2 Proposed Approach -- 2.1 Motivating the Need of Feature-Fusion |
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2.2 Implementing Aggregation Plugins in SSD-VGG16 Model -- 3 Results and Analysis -- 3.1 Experimental Platform -- 3.2 Results -- 4 Conclusion -- References -- Abstract Echo State Networks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Local Robustness -- 3.2 Abstract Interpretation -- 3.3 Echo State Networks -- 3.4 Abstract Training -- 3.5 Experiment -- 4 Results and Discussion -- 5 Conclusion -- References -- Minimal Complexity Support Vector Machines -- 1 Introduction -- 2 L1 Support Vector Machines and Minimal Complexity Machines -- 2.1 L1 Support Vector Machines |
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2.2 Minimal Complexity Machines -- 3 Minimal Complexity L1 Support Vector Machines -- 3.1 Architecture -- 3.2 KKT Conditions -- 3.3 Variant of Minimal Complexity Support Vector Machines -- 4 Computer Experiments -- 4.1 Comparison Conditions -- 4.2 Two-Class Problems -- 4.3 Multiclass Problems -- 5 Conclusions -- References -- Named Entity Disambiguation at Scale -- 1 Introduction -- 2 Related Work -- 3 Model Description -- 4 Experimental Results -- 5 Ablation -- 6 Conclusion -- References -- Applications -- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds |
| Summary |
This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications |
| Notes |
International conference proceedings |
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"This volume contains the papers presented at the 9th IAPR TC3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR 2020), held in virtual format and organized by Zurich University of Applied Sciences ZHAW, Switzerland, during September 2-4, 2020." |
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Includes author index |
| Subject |
Neural networks (Computer science) -- Congresses
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Pattern recognition systems -- Congresses
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Image processing.
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Data mining.
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Pattern recognition.
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Computer vision.
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Artificial intelligence.
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Computers -- Computer Graphics.
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Computers -- Database Management -- Data Mining.
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Computers -- Computer Vision & Pattern Recognition.
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Computers -- Intelligence (AI) & Semantics.
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Neural networks (Computer science)
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Pattern recognition systems
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Schilling, F.-P. (Frank-Peter)
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Stadelmann, Thilo.
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| ISBN |
9783030583095 |
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3030583090 |
