Linear Dynamical Systems (LDS)

For more info about our services contact : help@bestpfe.com

Table of contents

Author’s publications
Abstract
Acknowledgment
List of Figures
List of Tables
1 Introduction
1.1 Dynamic textures: definition, challenges, and applications
1.2 An overview of representing DTs based on dense trajectories
1.3 An overview of representing DTs based on moment-based features
1.4 An overview of representing DTs based on Gaussian-filtered features
1.5 Our main contributions
1.6 Outline of thesis
2 Literature review
2.1 Introduction
2.2 Optical-flow-based methods
2.2.1 A brief of optical-flow concept
2.2.2 Analyzing DTs based on optical flow
2.3 Model-based methods
2.3.1 Linear Dynamical Systems (LDS)
2.3.2 Modeling DTs based on LDS
2.4 Geometry-based methods
2.4.1 A brief of fractal analysis
2.4.2 DT representation based on fractal analysis
2.5 Learning-based methods
2.5.1 Deep-learning-based techniques
2.5.2 Dictionary-learning-based techniques
2.6 Filter-based methods
2.6.1 DT description based on learned filters
2.6.2 DT description based on non-learned filters
2.7 Local-feature-based methods
2.7.1 A brief of LBP
2.7.2 A completed model of LBP (CLBP)
2.7.3 Completed local structure patterns (CLSP), a variant of CLBP
2.7.4 LBP-based variants for textural image description
2.7.5 LBP-based variants for DT representation
2.8 Datasets and protocols for evaluations of DT recognition
2.8.1 UCLA dataset
2.8.2 DynTex dataset
2.8.3 DynTex++ dataset
2.8.4 DTDB dataset
2.9 Classifiers for evaluating DT representation
3 Proposed variants of LBP-based operators
3.1 Introduction
3.2 Completed AdaptIve Patterns (CAIP)
3.3 Some extensions of Local Derivative Patterns (xLDP)
3.3.1 Local Derivative Patterns
3.3.2 Adaptative directional thresholds
3.3.3 Completed model of LDP
3.3.4 Assessing our proposed extensions of LDP
3.4 Some extensions of local vector patterns (xLVP)
3.4.1 Local Vector Patterns
3.4.2 Adaptive directional vector thresholds
3.4.3 A completed model of LVP
3.5 Local Rubik-based Patterns (LRP)
3.5.1 Complemented components
3.5.2 Construction of LRP patterns
3.6 Completed HIerarchical LOcal Patterns (CHILOP)
3.6.1 Construction of CHILOP
3.6.2 A particular degeneration of CHILOP into CLBP
3.6.3 Beneficial properties of CHILOP operator
3.7 Summary
4 Representation based on dense trajectories
4.1 Introduction
4.2 Dense trajectories
4.3 Beneficial properties of dense trajectories
4.3.1 Directional features of a beam trajectory
4.3.2 Spatio-temporal features of motion points
4.4 Directional dense trajectory patterns for DT representation
4.4.1 Proposed DDTP descriptor
4.4.2 Computational complexity of DDTP descriptor
4.5 Experiments and evaluations
4.5.1 Experimental settings
4.5.2 Experimental results
4.5.2.1 Recognition on UCLA dataset
4.5.2.2 Recognition on DynTex dataset
4.5.2.3 Recognition on DynTex++ dataset
4.5.3 Global discussion
4.6 Summary
5 Representation based on moment models
5.1 Introduction
5.2 Moment models
5.2.1 Moment images
5.2.2 A novel moment volumes
5.2.3 Advantages of moment volume model
5.3 DT representation based on moment images
5.4 DT representation based on moment volumes
5.4.1 Proposed momental directional descriptor
5.4.2 Enhancing the performance with max-pooling features
5.5 Experiments and evaluations
5.5.1 Experimental settings
5.5.2 Assessment of effectiveness of moment models
5.5.3 Experimental results of MDP-based descriptors
5.5.3.1 Recognition on UCLA dataset
5.5.3.2 Recognition on DynTex dataset
5.5.3.3 Recognition on Dyntex++ dataset
5.5.3.4 Assessing the proposed components: Recognition with MDP-B and LDP-TOP
5.5.3.5 Assessing impact of max-pooling features: Recognition with EMDP descriptor
5.5.4 Global discussion
5.6 Summary
6 Representation based on variants of Gaussian filterings
6.1 Introduction
6.1.1 Motivation
6.1.2 A brief of our contributions
6.2 Gaussian-based filtering kernels
6.2.1 A conventional Gaussian filtering
6.2.2 Gradients of a Gaussian filtering kernel
6.3 A novel kernel based on difference of Gaussian gradients
6.3.1 Definition of a novel DoDG kernel
6.3.2 Beneficial properties of DoDG compared to DoG
6.4 Representation based on completed hierarchical Gaussian features
6.4.1 Construction of Gaussian-filtered CHILOP descriptor
6.4.2 Experiments and evaluations
6.4.2.1 Parameters for experimental implementation
6.4.2.2 Assessments of CHILOP’s performances
6.5 Representation based on RUbik Blurred-Invariant Gaussian features
6.5.1 Benefits of Gaussian-based filterings
6.5.2 Construction of RUBIG descriptor
6.5.3 Experiments and evaluations
6.5.3.1 Parameters for experimental implementation
6.5.3.2 Assessments of RUBIG’s performances
6.6 Representation based on Gaussian-filtered CAIP features
6.6.1 Completed sets of Gaussian-based filtered outcomes
6.6.2 Beneficial properties of filtered outcomes 2D=3D ;0
6.6.3 DT description based on complementary filtered outcomes 2D=3D ;0
6.6.4 Experiments and evaluations
6.6.4.1 Parameters for experimental implementation
6.6.4.2 Assessments of DoG-based features compared to those of FoSIG and V-BIG
6.6.4.3 Assessments of LOGIC2D=3D’s performances
6.7 Representation based on oriented magnitudes of Gaussian gradients
6.7.1 Oriented magnitudes of Gaussian gradients
6.7.2 DT representation based on oriented magnitudes
6.7.3 Experiments and evaluations
6.7.3.1 Parameters for experimental implementation
6.7.3.2 Assessments of effectiveness of decomposing models
6.7.3.3 Assessments of MSIOMFk;D4 and MSVOMFk;D4
6.8 Representation based on Gaussian-gradient features
6.8.1 High-order Gaussian-gradient Filtered Components
6.8.2 DT Representation Based on 2D=3D H; Components
6.8.3 Experiments and evaluations
6.8.3.1 Parameters for experimental implementation
6.8.3.2 Assessments of High-order Gaussian-gradient Descriptors
6.8.3.3 Comprehensive Comparison to Non-Gaussian-gradients
6.9 Representation based on DoDG-filtered features
6.9.1 Construction of DoDG-filtered descriptors
6.9.2 Experiments and evaluations
6.9.2.1 Parameters for experimental implementation
6.9.2.2 Assessments of DoDG-based descriptors
6.9.2.3 Comprehensive comparison to DoG-based descriptors
6.10 Comprehensive evaluations in comparison with existing methods
6.10.1 Benefits of Gaussian-based filterings
6.10.1.1 Robustness to the well-known issues of DT description
6.10.1.2 Rich and discriminative features of Gausian-gradient-based filterings .
6.10.2 Complexity of our proposed descriptors
6.10.3 Comprehensive discussions of DT classification on different datasets
6.10.3.1 Classification on UCLA
6.10.3.2 Classification on DynTex
6.10.3.3 Classification on DynTex++
6.10.3.4 Classification on DTDB dataset
6.11 Global discussions
6.11.1 Further evaluations for Gaussian-gradient-based descriptors
6.11.2 Evaluating appropriation of our proposals for real applications
6.12 Summary
7 Conclusions and perspectives
7.1 Conclusions
7.2 Perspectives
Bibliography