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基于计算智能的图像配准与分割研究

摘要第5-6页
ABSTRACT第6-7页
Notations第12-13页
Abbreviations第13-17页
Chapter 1 Introduction第17-27页
    1.1 Background第17-24页
        1.1.1 Image segmentation第17-20页
        1.1.2 Image registration第20-21页
        1.1.3 Graph matching and swarm intelligence第21-24页
    1.2 Organization of the dissertation第24-27页
Chapter 2 Fuzzy Active Contour Model with Kernel Metric for Image Segmentation第27-49页
    2.1 Background第27-29页
    2.2 Previous work第29-32页
        2.2.1 Chan-Vese model第29-31页
        2.2.2 Fuzzy active contour model第31-32页
    2.3 Fuzzy active contour model with kernel metric第32-37页
        2.3.1 Energy formulation第32-33页
        2.3.2 Energy minimization第33-35页
        2.3.3 Numerical implementation第35-37页
    2.4 Experimental results第37-44页
        2.4.1 Experiments on synthetic image第38页
        2.4.2 Experiments on remote sensing images and medical image第38-39页
        2.4.3 Experiments on natural images第39-44页
    2.5 Conclusions第44-49页
Chapter 3 Remote Sensing Image Registration with Spatial Restraint Based on Mo-ment Invariants and Fast Generalized Fuzzy Clustering第49-65页
    3.1 Background第49-50页
    3.2 Motivation第50-54页
        3.2.1 Spatial restraint第51-52页
        3.2.2 Moment invariants第52页
        3.2.3 Fast generalized fuzzy c-means (FGFCM)第52-54页
    3.3 Framework of the proposed method第54-57页
        3.3.1 FGFCM for segmenting target and reference Images第55页
        3.3.2 Characteristics features of objects第55-56页
        3.3.3 Matching objects第56-57页
        3.3.4 Keypoint matching第57页
        3.3.5 Outlier removal第57页
    3.4 Experimental results第57-61页
        3.4.1 Results on multi-spectral image registration第59页
        3.4.2 Results on multi-temporal image registration第59-60页
        3.4.3 Results on multi-sensor image registration第60-61页
    3.5 Conclusions第61-65页
Chapter 4 Remote Sensing Image Registration Based on Fast Sample Consensus第65-77页
    4.1 Background第65-66页
    4.2 Description of the proposed method第66-70页
        4.2.1 Fast sample consensus第66-68页
        4.2.2 Iterative selection of correct matches第68-69页
        4.2.3 Removal of the imprecise points第69-70页
    4.3 Experimental results第70-75页
        4.3.1 Parameter setting第70-71页
        4.3.2 Experimental settings第71-73页
        4.3.3 Complexity analysis第73页
        4.3.4 Experimental results第73-75页
    4.4 Conclusions第75-77页
Chapter 5 High-Order Graph Matching Based on Ant Colony Optimization第77-93页
    5.1 Background第77-78页
    5.2 Related background第78-81页
        5.2.1 High-order graph matching第78-79页
        5.2.2 Ant colony optimization第79-81页
    5.3 High-order graph matching based on ACO第81-86页
        5.3.1 Heuristic factor第82页
        5.3.2 Pheromone information第82-84页
        5.3.3 Transition probability第84页
        5.3.4 Implementation第84-86页
    5.4 Experimental results第86-88页
        5.4.1 Synthetic datasets第87页
        5.4.2 Real-world datasets第87-88页
    5.5 Conclusion第88-93页
Chapter 6 High-Order Graph Matching Based on Discrete Particle Swarm Optimiza-tion第93-113页
    6.1 Background第93-95页
    6.2 Related background第95-97页
        6.2.1 High-order graph matching第95-96页
        6.2.2 Particle swarm optimization第96-97页
    6.3 High-order graph matching based on discrete particle swarm optimization第97-104页
        6.3.1 Framework of the proposed method第97页
        6.3.2 Definition of discrete position and velocity第97-99页
        6.3.3 Velocity updating第99-101页
        6.3.4 Position updating第101页
        6.3.5 Heuristic information and initialization第101-103页
        6.3.6 Local search第103页
        6.3.7 Complexity analysis第103-104页
    6.4 Experimental results第104-111页
        6.4.1 Experimental settings第104-105页
        6.4.2 Experiments on synthetic datasets第105-108页
        6.4.3 Experiments on CMU House dataset第108-109页
        6.4.4 Experiments on natural images第109-111页
    6.5 Conclusions第111-113页
Chapter 7 Conclusion第113-115页
    7.1 Thesis conclusion第113页
    7.2 Future directions and challenges第113-115页
References第115-127页
Thanks第127-129页
Resume第129-130页

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