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Object Detection using Feature Mining in a Distributed Machine Learning Framework / Titelei/Inhaltsverzeichnis
Object Detection using Feature Mining in a Distributed Machine Learning Framework / Titelei/Inhaltsverzeichnis
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Chapter
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I–XIV
Titelei/Inhaltsverzeichnis
I–XIV
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1–15
1 Introduction
1–15
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16–27
2 Related Work and Data Sets
16–27
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2.1 Machine Learning for Visual Object Detection
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2.1.1 Feature Provision
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2.1.2 Learning Algorithms
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2.2 Data Sets and Benchmarks
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28–61
3 Fundamentals
28–61
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3.1 Common Features
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3.1.1 Haar-like Features
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3.1.2 Histograms of Oriented Gradients
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3.1.3 FromFeatures to Classifiers
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3.2 Supervised Machine Learning
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3.2.1 Adaptive Boosting
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3.2.2 Viola and Jones Detection Framework
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3.2.3 Margin Analysis
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3.2.4 Variants of Boosting Algorithms
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3.3 Data Analysis
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3.3.1 Cluster Analysis
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3.3.2 Principal Component Analysis
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3.4 Detector PerformanceMeasures
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62–67
4 Distributed Machine Learning Framework
62–67
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68–75
5 Learning from Sparse Training Data
68–75
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5.1 Training Data Augmentation
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5.2 Experimental Results
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5.2.1 Experiments on Face Detection
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5.2.2 Experiments on Cell Data Set
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5.3 Discussion
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76–89
6 Fractal Integral Paths
76–89
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6.1 Boosted Fractal Integral Paths
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6.1.1 Fractals
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6.1.2 Fractal Features
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6.1.3 Fractal Properties
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6.1.4 Construction of Fractals
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6.1.5 Feature Types
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6.2 Experimental Results
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6.2.1 Face Detection
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6.2.2 Microscopic Cell Detection
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6.2.3 Training and Computing Time
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6.3 Discussion
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90–120
7 Multi-Feature Mining for Detector Learning
90–120
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7.1 2Rec Features
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7.2 Keypoint HOG Features
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7.3 Experimental Results
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7.3.1 Face Detection
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7.3.2 Lateral Car Detection
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7.3.3 Pedestrian Detection
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7.3.4 Insights into the Training Process
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7.4 Discussion
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121–130
8 Non-Maximum Suppression using Dempster’s Theory of Evidence
121–130
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8.1 Merging Multiple Detections based on Dempster’s Theory
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8.1.1 Cascaded Classifier
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8.1.2 Dempster-Shafer Theory of Evidence
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8.1.3 Joint Confidence based on Dempster-Shafer
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8.1.4 Confidence-based Detection Merging
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8.2 Experimental Results
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8.2.1 Face Detection
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8.2.2 Lateral Car Detection
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8.3 Discussion
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131–134
9 Conclusion
131–134
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135–137
A Lindenmayer Systems Defining Fractal Integral Paths
135–137
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A.1 L-System Defining Gosper Curve
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A.2 L-System Defining E-Curve
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138–152
Bibliography
138–152
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Object Detection using Feature Mining in a Distributed Machine Learning Framework , page I - XIV
Titelei/Inhaltsverzeichnis
Autoren
Arne Ehlers
DOI
doi.org/10.51202/9783186855107-I
ISBN print: 978-3-18-385510-0
ISBN online: 978-3-18-685510-7
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doi.org/10.51202/9783186855107-I
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