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Visual Motion Processing / Titelei/Inhaltsverzeichnis
Visual Motion Processing / Titelei/Inhaltsverzeichnis
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Chapter
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I–XII
Titelei/Inhaltsverzeichnis
I–XII
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1–11
1 Introduction
1–11
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1.1 Biological Motion Recognition
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1.1.1 Temporal and View-Point Variations
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1.1.2 Discriminative Features
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1.2 Computational Models for Biological Motion Recognition
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1.2.1 Computational Neuroscience
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1.3 Summary & Thesis Structure
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12–25
2 Computational Model
12–25
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2.1 Biological Motion Recognition in the Brain
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2.1.1 Different Motion Representations
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2.1.2 Static and Dynamic Form Description
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2.1.3 Neurophysiological Experiments
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2.1.4 Brain Areas based on Neurophysiological Experiments
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2.1.5 Dorsal Stream Areas In Biological Motion Recognition
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2.1.6 Mid-Level Motion Patterns
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2.1.7 Ventral Stream Areas In Biological Motion Recognition
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2.1.8 Posterior Superior Temporal Sulcus (STSp)
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2.2 Proposed Computational Model
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2.2.1 Feed-Forward Neural Networks
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2.2.2 Invariance Properties of Feed-Forward Neural Networks
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2.2.3 RelatedWork
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2.2.4 Proposed Computational Model
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26–57
3 Unsupervised Pattern Learning
26–57
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3.1 RelatedWork
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3.1.1 Principal Component Analysis
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3.1.2 Independent Component Analysis
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3.1.3 Extensions of NMF
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3.2 Properties of Parts-based Representations
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3.2.1 Basic Constraints
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3.2.2 Non-negativity
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3.2.3 Sparsity
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3.2.4 Local and Lateral Inhibition
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3.2.5 Resulting Energy Function and Notations
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3.3 Sparse Non-negative Matrix Factorization
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3.3.1 Sparse Activations
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3.3.2 Normalized Basis Vectors
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3.3.3 Sparse Basis Vectors
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3.3.4 Reconstruction Energy
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3.3.5 sNMF Learning Algorithm
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3.3.6 Orthogonality and Enforced Parts-Basedness
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3.4 Non-negative Representations of Real-valued Data
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3.4.1 Multidimensional Input
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3.4.2 Multidimensional Basis Vectors
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3.4.3 Multidimensional Activations
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3.4.4 Sparse Activation Amplitudes
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3.4.5 Positive and Negative Input
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3.4.6 Strict Non-negativity
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3.4.7 Weak Non-negativity
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3.4.8 Orthogonality between Positive and Negative Reconstructions
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3.5 Translation-invariant NMF
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3.5.1 Reconstruction Energy
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3.5.2 Sparse Activations
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3.5.3 Orthogonality between Positive and Negative Representation
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3.5.4 Enforced Topological Sparsity
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3.5.5 VNMF Learning Algorithm
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3.6 AlgorithmSummary
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58–88
4 Optical Flow Estimation
58–88
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4.1 ProblemFormulation
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4.1.1 General Algorithmic Approaches
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4.1.2 Correlation Methods
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4.1.3 Differential Methods
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4.1.4 Method Comparison
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4.2 RelatedWork
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4.2.1 Horn and Schunk
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4.2.2 Lukas and Kanade
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4.2.3 Extensions of the Classical Methods
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4.2.4 Multi-Scale Methods
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4.2.5 Other OFE-algorithms
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4.3 VNMF-OFE Approach
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4.3.1 Restrict Optical Flow Field to Model
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4.3.2 Enforced Non-Negativity
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4.3.3 Penalize Opposing Directions
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4.3.4 Sparse Activity Amplitudes
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4.3.5 Lateral Competition
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4.3.6 VNMF-OFE Learning Algorithm
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4.3.7 VNMF-OFE Algorithm for Activation Inference
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4.4 Learning the Basis Vectors
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4.4.1 Varying Model Parameters
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4.4.2 Varying Energy Parameters
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4.4.3 Learned vs Designed Basis Vectors
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4.4.4 Discussion of the Parameter Settings
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4.5 Comparison & Results
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4.5.1 Comparison to Related Work
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4.5.2 VNMF-OFE for Human Actions
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4.6 Summary & Discussion
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89–115
5 Feature Extraction
89–115
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5.1 Optical Flow Patterns
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5.1.1 Preprocessing
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5.1.2 Varying Energy Parameters
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5.1.3 Varying Basis Vector Parameters
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5.1.4 Detailed Analysis of the Learning Process
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5.1.5 Comparison to PCA and sNMF
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5.1.6 Basis Vectors learned on Face Data
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5.2 Gradient Patterns
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5.2.1 Preprocessing
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5.2.2 Varying Energy Parameters
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5.2.3 Varying Basis Vector Parameters
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5.2.4 Detailed Analysis of the Learning Process
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5.2.5 Comparison to PCA and sNMF
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5.2.6 Basis Vectors learned on Face Data
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5.3 VNMF as Feature Descriptor
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5.3.1 Simple Cell Response
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5.3.2 Complex Cell Response
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5.3.3 Relation to HOG/HOF Descriptor
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116–125
6 Human Action Recognition
116–125
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6.1 Support Vector Machine (SVM)
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6.2 Results for Different Basis Vector Sets
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6.2.1 Varying Basis Vector Parameters
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6.2.2 Varying Energy Parameters
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6.2.3 Comparison to PCA and sNMF Patterns
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6.2.4 Varying Simple Cell Response
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6.3 Facial Expression Recognition
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6.4 Comparison to Related Work
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6.4.1 HOG/HOF Results
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6.4.2 Benchmark Results
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126–131
7 Conclusion
126–131
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7.1 Summary & Discussion
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7.1.1 Optical Flow Estimation (VNMF-OFE)
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7.1.2 Feature Extraction (VNMF)
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7.1.3 Biological Motion Recognition Model (FFNN)
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7.2 Outlook
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132–132
A Bag of Words
132–132
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133–134
B Visual Cortex
133–134
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135–136
C Gradient Derivations
135–136
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C.1 Translation Invariant Learning
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C.2 Topological Sparsity
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137–142
D Sparse Non-Negative Linear Dynamic Systems
137–142
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D.1 Temporal Extension of sNMF
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D.2 RelatedWork
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D.3 Transition Energy
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D.3.1 Sparsity in the Transitions
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D.3.2 sNN-LDS Learning Algorithm
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D.4 Results
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143–158
Bibliography
143–158
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Visual Motion Processing , page I - XII
Titelei/Inhaltsverzeichnis
Autoren
Thomas Guthier
DOI
doi.org/10.51202/9783186251084-I
ISBN print: 978-3-18-525108-5
ISBN online: 978-3-18-625108-4
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