55 research outputs found
Determination of the normalization level of database schemas through equivalence classes of attributes
In this paper, based on equivalence classes of attributes there are formulated necessary and sufficient conditions that constraint a database schema to be in the second, third or Boyce-Codd normal forms. These conditions offer a polynomial complexity for the testing algorithms of the normalizations level
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Έμ€.Estimating human poses from images is one of the fundamental tasks in computer vision, which leads to lots of applications such as action recognition, human-computer interaction, and virtual reality. Especially, estimating 3D human poses from 2D inputs is a challenging problem since it is inherently under-constrained. In addition, obtaining 3D ground truth data for human poses is only possible under the limited and restricted environments. In this dissertation, 3D human pose estimation is studied in different aspects focusing on various types of the availability of the data. To this end, three different methods to retrieve 3D human poses from 2D observations or from RGB images---algorithms of 3D reconstruction, weakly-supervised learning, and supervised learning---are proposed.
First, a non-rigid structure from motion (NRSfM) algorithm that reconstructs 3D structures of non-rigid objects such as human bodies from 2D observations is proposed. In the proposed framework which is named as Procrustean Regression, the 3D shapes are regularized based on their aligned shapes. We show that the cost function of the Procrustean Regression can be casted into an unconstrained problem or a problem with simple bound constraints, which can be efficiently solved by existing gradient descent solvers. This framework can be easily integrated with numerous existing models and assumptions, which makes it more practical for various real situations. The experimental results show that the proposed method gives competitive result to the state-of-the-art methods for orthographic projection with much less time complexity and memory requirement, and outperforms the existing methods for perspective projection.
Second, a weakly-supervised learning method that is capable of learning 3D structures when only 2D ground truth data is available as a training set is presented. Extending the Procrustean Regression framework, we suggest Procrustean Regression Network, a learning method that trains neural networks to learn 3D structures using training data with 2D ground truths. This is the first attempt that directly integrates an NRSfM algorithm into neural network training. The cost function that contains a low-rank function is also firstly used as a cost function of neural networks that reconstructs 3D shapes. During the test phase, 3D structures of human bodies can be obtained via a feed-forward operation, which enables the framework to have much faster inference time compared to the 3D reconstruction algorithms.
Third, a supervised learning method that infers 3D poses from 2D inputs using neural networks is suggested. The method exploits a relational unit which captures the relations between different body parts. In the method, each pair of different body parts generates relational features, and the average of the features from all the pairs are used for 3D pose estimation. We also suggest a dropout method called relational dropout, which can be used in relational modules to impose robustness to the occlusions. The experimental results validate that the performance of the proposed algorithm does not degrade much when missing points exist while maintaining state-of-the-art performance when every point is visible.RGB μμμμμ μ¬λ μμΈ μΆμ λ°©λ²μ μ»΄ν¨ν° λΉμ λΆμΌμμ μ€μνλ©° μ¬λ¬ μ΄ν리μΌμ΄μ
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νμλ€.Abstract i
Contents iii
List of Tables vi
List of Figures viii
1 Introduction 1
1.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.1 3D Reconstruction of Human Bodies . . . . . . . . . . 9
1.4.2 Weakly-Supervised Learning for 3D HPE . . . . . . . . 11
1.4.3 Supervised Learning for 3D HPE . . . . . . . . . . . . 11
1.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Related Works 14
2.1 2D Human Pose Estimation . . . . . . . . . . . . . . . . . . . . 14
2.2 3D Human Pose Estimation . . . . . . . . . . . . . . . . . . . . 16
2.3 Non-rigid Structure from Motion . . . . . . . . . . . . . . . . . 18
2.4 Learning to Reconstruct 3D Structures via Neural Networks . . 23
3 3D Reconstruction of Human Bodies via Procrustean Regression 25
3.1 Formalization of NRSfM . . . . . . . . . . . . . . . . . . . . . 27
3.2 Procrustean Regression . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 The Cost Function of Procrustean Regression . . . . . . 29
3.2.2 Derivatives of the Cost Function . . . . . . . . . . . . . 32
3.2.3 Example Functions for f and g . . . . . . . . . . . . . . 38
3.2.4 Handling Missing Points . . . . . . . . . . . . . . . . . 43
3.2.5 Optimization . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.6 Initialization . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Orthographic Projection . . . . . . . . . . . . . . . . . 46
3.3.2 Perspective Projection . . . . . . . . . . . . . . . . . . 56
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 Weakly-Supervised Learning of 3D Human Pose via Procrustean Regression Networks 69
4.1 The Cost Function for Procrustean Regression Network . . . . . 70
4.2 Choosing f and g for Procrustean Regression Network . . . . . 74
4.3 Implementation Details . . . . . . . . . . . . . . . . . . . . . . 75
4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 77
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5 Supervised Learning of 3D Human Pose via Relational Networks 86
5.1 Relational Networks . . . . . . . . . . . . . . . . . . . . . . . 88
5.2 Relational Networks for 3D HPE . . . . . . . . . . . . . . . . . 88
5.3 Extensions to Multi-Frame Inputs . . . . . . . . . . . . . . . . 91
5.4 Relational Dropout . . . . . . . . . . . . . . . . . . . . . . . . 93
5.5 Implementation Details . . . . . . . . . . . . . . . . . . . . . . 94
5.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 95
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6 Concluding Remarks 105
6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . 108
Abstract (In Korean) 128Docto
Data Update and Model Revision for Soil Profile Analytical Database of Europe of Measured Parameters (SPADE/M2)
The Soil Profile Analytical Database of Europe of Measured parameters (SPADE/M) is part of the distribution package of the Soil Geographic Database of Eurasia (SGDBE). Typical combinations of profile parameters and morphological characteristics of the sample site were intended to support the definition of generalized rules for estimating pedological and hydrological properties of the pedo-transfer rule (PTR) database of the SGDBE. In 2005 the data of the SGDBE were transferred to a common data storage structure. In 2008 original hard-copies on profile measurements were re-discovered at the National Soil Resources Institute, Cranfield University (NSRI). To make the original data more generally available the profiles were added to the existing database. This step required changes to the structure of the database and a validation of the all entries for accurate and reliable data storage and retrieval.JRC.DDG.H.7-Land management and natural hazard
A molecular dynamics simulation of the effect of shear on molecular orientation
The focus of this work is the investigation of the effect of shear produced by buoyancy-induced convection on the orientation of rod-like molecules. The prob-lem is a mathematical simulation of a physical problem dealing with the growth of a polymer film which is under examination for use in fiber optic communication. The Molecular Dynamics simulation method was employed to solve the problem. A computer program was developed to implement the simulation and the details of the simulation development are discussed. The results of the atomic simulations that were used to validate the code are presented first. The simulation results for molecules and mixtures of molecules and atoms are then presented. It was determined that the shear caused by the velocity field does have an effect on the orientation of the rod-like molecules. However, the magnitude of this effect was seen to be sensitive to the type of the molecule, the concentration of the solution, and the properties of the wall
Ontology based data warehousing for mining of heterogeneous and multidimensional data sources
Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals
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