在工業(yè)4.0時(shí)代,工業(yè)計(jì)算機(jī)視覺(jué)及其相關(guān)應(yīng)用日益獲得成功和普及。由于多媒體數(shù)據(jù)規(guī)模的增加和算法的復(fù)雜性更加,它在增強(qiáng)計(jì)算機(jī)視覺(jué)算法中起著至關(guān)重要的作用。本研究引入了多種模型,其中介紹了最新的Spearman相關(guān)分析算法（帶Rank的典型相關(guān)分析）及其代數(shù)擴(kuò)展和深度學(xué)習(xí)模型。此外,這其中的大多數(shù)模型都受到了遷移學(xué)習(xí)方法的啟發(fā)。本模型引入了非線性多維數(shù)據(jù)集,由于數(shù)據(jù)的非線性,多維數(shù)據(jù)及其對(duì)應(yīng)的應(yīng)用程序面臨多個(gè)挑戰(zhàn)。本文提出的模型通過(guò)與問(wèn)題相關(guān)的數(shù)據(jù)集的分析,提出了針對(duì)復(fù)雜性問(wèn)題的非線性優(yōu)化和降維的解決方案。本研究主要分為以下三個(gè)部分:首先介紹了Spearman相關(guān)算法的內(nèi)核擴(kuò)展,涉及多維數(shù)據(jù)集的非線性問(wèn)題,從一維Spearman相關(guān)分析算法到擴(kuò)展的二維Spearman相關(guān)分析算法,進(jìn)而擴(kuò)展為三維Spearman相關(guān)分析算法等。此外,還介紹了Spearman相關(guān)算法及其擴(kuò)展算法在具有遷移學(xué)習(xí)方法模型中的運(yùn)用。然后,第二部分將提出的Spearman相關(guān)算法進(jìn)一步擴(kuò)展并用于多維數(shù)據(jù)集的信息投影和多視圖非線性問(wèn)題的相關(guān)模型構(gòu)建。最后,利用本文提出的Spearman相關(guān)擴(kuò)展算法解決了數(shù)據(jù)及圖像分辨率... 

【文章來(lái)源】：江蘇大學(xué)江蘇省

【文章頁(yè)數(shù)】：166 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
Abstract
List of Abbreviations
Chapter 1 Introduction
    1.1.Background
    1.2.Problem Statements and Motivations of Proposed Models
    1.3.Contributions of the Dissertation
    1.4.Implementations of Spearman Correlation Analysis in Proposed Models
        1.4.1.Nonlinear Data Driven Processing Via Multi-Dimensional Spearman Correlation Analysis
        1.4.2.Multi-Dimension Projection for Non-Linear Data via Spearman Correlation Analysis(MD-SCA)
        1.4.3.Non-Linear Optimizations Among Low Resolution and High Resoulation Image Via Spearman Correlation Analysis
    1.5.Organization of the Dissertation
Chapter 2 Literature Review
    2.1.Correlation Analysis
        2.1.1.Pearson Correlation Analysis
        2.1.2.Spearman Correlation Analysis
        2.1.3.Kendall Rank Correlation
        2.1.4.Basic Difference between Spearman Correlation Analysis and Kendall Rank Correlation
    2.2.Multivariate Extension of Correlation Analysis
        2.2.1.Canonical Correlation Analysis
        2.2.2.Canonical Correlation Analysis with Ranks
    2.3.Spearman Correlation Analysis for Image-To-Video Data Driven
        2.3.1.Dimension Reduction Approaches for Image-To-Video
        2.3.2.Spearman Correlation Analysis Supporting Frameworks and Libraries
    2.4.Multilinear Subspace Learning Algorithms
    2.5.Systametic Literature Review Inspired by Presented Models and Motivation
        2.5.1.Nonlinear Data Driven Processing Via Multi-Dimensional Spearman Correlation Analysis
        2.5.2.Multi-Dimension Projection for Non-Linear Data via Spearman Correlation Analysis(MD-SCA)
        2.5.3.Non-Linear Optimizations Among Low Resolution and High Resoulation Image Via Spearman Correlation Analysis
        2.5.4.Conclusion and Summary
Chapter 3 Nonlinear Data Driven Processing Via Multi-Dimensional Spearman Correlation Analysis
    3.1.Transfer Learning
    3.2.Implementation of Classical One-Dimensional Spearman Correlation Analysis with Transfer Learning Approach for Background and Scene Modelling
    3.3.The Systametic Approach of Proposed Model
        3.3.1.Overlapped Camera”EPFL”Video Dataset
        3.3.2.Video Structural and Semantic Segmentation
        3.3.3.Video Discription of Overlapped Scene
        3.3.4.Videos’Deep Visual Features Extraction
        3.3.5.Video Analysis for Background and Foreground via Spearman Correlation Analysis
        3.3.6.Implementation of Pairwise Cosine Distance on Videos’Rank Correlations
        3.3.7.Results
        3.3.8.Disscussion and Analysis
    3.4.Implementation of Spearman Correlation Analysis on Multi-Domain Datasets
        3.4.1.Hyperparameters Optimization in Deep Learning Models
        3.4.2.Bench Mark Datasets for Learning Montone Conditions
        3.4.3.Heart Disease
        3.4.4.Breast Cancer Wisconsin
        3.4.5.Liver Disorders
        3.4.6.Vehicle Silhouettes
        3.4.7.Glass
        3.4.8.Titanic
        3.4.9.Result Optimization of the Bench Mark Datasets Under the ROC Curve
    3.5.Multivariate Extension as Two-Dimensional Spearman Correlation Analysis(2D-SCA)
        3.5.1.Introduced Two-Dimensional Spearman Correlation Analysis
        3.5.2.Implementation of Proposed Approach on Two-Dimensional Data
        3.5.3.Setting and Primitives of Implementations
        3.5.4.Results
        3.5.5.Disscusion and Analysis
    3.6.Deep Three Dimensional Spearman Correlation Analysis(D3D-SCA)
        3.6.1.Customized Inception-V3
        3.6.2.Contribution of Spearman Correlation Analysis for Video Analysis
        3.6.3.The Novel Three-Dimensional Spearman Correlation Analysis
    3.7.Customized Xception Classifiers
    3.8.Experiment
        3.8.1.Industrial Product Datasets Encoded as Mode Flattening3D(Video)Pattern Data
        3.8.2.Image Frame Blocks for Visual Feature Maps
        3.8.3.Implementation of Pairwise D3D-Spearman Correlation
        3.8.4.Classification and Auto-Update of Model
        3.8.5.Application Deployed Server and Framework
    3.9.Results
    3.10.Conclusion and Analysis
Chapter 4 Multi-Dimension Projection for Non-Linear Data Via Spearman Correlation Analysis(MD-SCA)
    4.1.Introduction
    4.2.Problem Statements and Motivation
    4.3.Extension for Multi-Dimensional Informative Projections via Spearman Correlation Analysis(MD-SCA)
    4.4.Preliminaries of Multi-Dimensional Projection via Spearman Correlation Analysis(MD-SCA)
        4.4.1.Introduced Extension of Spearman Correlation Analysis for Multi-Dimensional Informative Projections
        4.4.2.Transformation from the Dual Representation Theory
    4.5.Implementation
        4.5.1.Dataset
        4.5.2.Deep Visual Features Map
        4.5.3.Implementation of MD-SCA in Proposed Deep Learning Model
    4.6.Server and Framework Specification
    4.7.Results
    4.8.Discussion Analysis and Conclusion
Chapter 5 Non-Linear Optimizations Among Low Resolution and High Resoulation Image Via Spearman Correlation Analysis
    5.1.Introduction
    5.2.Introduce Systematic Architecture of Proposed Model and Implementation
        5.2.1.Rule Induction of Proposed Deep Learning Model
        5.2.2.Setting of Hyperpermters in Convolutional Neural Network
        5.2.3.Convolutional Layers
        5.2.4.Tuning of Pooling Layer
        5.2.5.Tuning of Normalization Layers
        5.2.6.Notation of Spearman Correlation Analysis
        5.2.7.Extension of Spearman Correlation Analysis for Correlation between LR and HR Images
        5.2.8.Mapping Of Correlational Features from LR to Relative HR Correlational Features
        5.2.9.Radial Basis Function Network(RBFN)
        5.2.10.Xception Classifier
    5.3.Implementation of Deep Spearman Correlation Analysis
        5.3.1.MNIST Writing Dataset
        5.3.2.Reid Vehicle License Number Plate Dataset
    5.4.Server Specification
    5.5.Conclusion and Future Work
Chapter 6 Conclusion,Discussion and Analysis
    6.1.Conclusion with Consequences of Studies
    6.2.Future Work
References
Publications
Acknowledgement



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