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Temporally Consistent Superpixels / Titelei/Inhaltsverzeichnis
Temporally Consistent Superpixels / Titelei/Inhaltsverzeichnis
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Titelei/Inhaltsverzeichnis
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1–9
1 Introduction
1–9
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1.1 Motivation
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1.2 Related Works
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1.2.1 Supervoxel Methods
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1.2.2 Superpixel Methods
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1.3 Challenges
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1.4 Contributions
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1.5 Structure of this Thesis
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10–33
2 Fundamentals
10–33
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2.1 Image Segmentation
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2.1.1 MRF/CRF-based Image Segmentation
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2.1.2 Segmentation by Clustering
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2.1.3 SLIC Superpixels
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2.1.4 Mean Shift Superpixel Segmentation
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2.2 Optical Flow
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2.2.1 Horn-Schunck-Method
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2.2.2 Lucas-Kanade-Method
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2.2.3 Sparse-to-Dense Optical Flow Conversion
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2.2.4 Lucas/Kanade meets Horn/Schunck
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34–45
3 Superpixels with Temporal Consistency
34–45
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3.1 System Overview
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3.2 Feature Space Setup
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3.3 Hybrid Optimization
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3.4 Sliding Window
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46–63
4 Superpixel Propagation and Handling of Structural Changes
46–63
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4.1 Segmentation Propagation
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4.1.1 Propagation using Dense Optical Flow
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4.1.2 Efficiency Improvement through Sparse Optical Flow
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4.2 Handling of Structural Changes
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4.2.1 Size-based Handling
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4.2.2 Handling by Occlusion and Disocclusion Detection
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64–104
5 Experimental Results
64–104
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5.1 Evaluation of Temporally Consistent Superpixels
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5.1.1 Benchmark Metrics
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5.1.2 Data Sets and Experimental Setup
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5.1.3 Per Frame Segmentation Quality
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5.1.4 Spatio-Temporal Segmentation Quality
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5.1.5 Superpixel Label Consistency
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5.1.6 Complexity Considerations
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5.2 Demonstration: Interactive Video Segmentation
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5.2.1 Interactive Video Segmentation using Superpixels
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5.2.2 Segmentation Quality and Runtime Evaluation
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105–108
6 Conclusions
105–108
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109–122
Bibliography
109–122
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Temporally Consistent Superpixels
Titelei/Inhaltsverzeichnis
Autoren
Matthias Reso
DOI
doi.org/10.51202/9783186861108-I
ISBN print: 978-3-18-386110-1
ISBN online: 978-3-18-686110-8
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doi.org/10.51202/9783186861108-I
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