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Uncertainty quantification and its applications for multimodal semantic segmentation / eingereicht von Pascal Colling, M. Sc. Wuppertal, 2022
Content
Abstract
Acknowledgments
Foreword
Contents
Contents
1 Introduction
2 Fundamentals
2.1 Sensor and Data Types
2.2 Artificial Neural Networks
2.2.1 Feedforward Neural Networks
2.2.2 Convolutional Neural Networks
2.3 Object Recognition
2.3.1 Semantic Segmentation
2.3.2 Object Detection and Tracking
2.3.3 Metrics for Evaluation
2.4 Uncertainty Quantification and MetaSeg
2.4.1 Dispersion Measures and Segment-wise Aggregation
2.4.2 Metrics for Evaluation
3 Region-Based Active Learning using Priority Maps for Image Segmentation
3.1 Introduction
3.2 Related Work
3.3 A new Method for Region-Based Active Learning
3.3.1 Acquisition with Priority Maps
3.3.2 Joint Priority Maps Based on Prediction Quality Estimation and Click Estimation
3.4 Experiments and Results
3.4.1 Metrics for Evaluation
3.4.2 Experiment Settings
3.4.3 Evaluation
3.5 Discussion
4 False Positive Detection and Prediction Quality Estimation for Point Cloud Segmentation
4.1 Introduction
4.2 Related Work
4.3 A new Method for False Positive Detection and Prediction Quality Estimation
4.3.1 Pre-processing
4.3.2 Dispersion Measures and Segment-wise Aggregation
4.4 Experiments and Results
4.4.1 SemanticKITTI
4.4.2 NuScenes
4.4.3 Metric Selection
4.4.4 Class- and Category-wise Evaluation
4.4.5 Confidence Calibration
4.5 Discussion
5 HD Lane Map Creation based on Trail Map Aggregation
5.1 Introduction
5.2 Related Work
5.3 A new Method for HD Lane Map Generation
5.3.1 Detection and Tracking
5.3.2 Aggregation
5.3.3 Extraction
5.4 Experiments and Results
5.4.1 Metrics for Evaluation
5.4.2 Evaluation
5.5 Discussion and Future Work
6 Conclusion & Outlook
List of Figures
List of Tables
List of Algorithms
List of Notations
List of Abbreviations
Bibliography