超像素分割算法的稳健性分析与一致性评价

发布时间：2021-11-03 21:00

　　从计算机视觉到图像理解和多媒体分析,超像素得到学术界越来越多的关注。超像素生成是指根据局部像素的颜色、位置等特征对其进行分组。简而言之,同一超像素内的所有像素都具有相似或相同的特征。与像素相比,超像素包含更多的局部信息。此外,它们可以保持图像中大部分物体的边界。它为计算图像特征和降低后续图像处理任务的复杂度提供了一种简便的方法,从而引起了人们对计算机视觉的浓厚兴趣,同时它还可以显著地提高算法的处理效率。一个比较热门的研究方向是超像素分割,近年来已经提出了很多算法。这些算法使用不同的性能评估指标和数据集进行评估,从而导致算法之间的比较存在差异,这给研究人员比较前沿算法和评价其优缺点提供了一个参考。这些算法大多在无噪声的自然图像上进行计算,得到了很好的结果。然而,目前针对自然图像中常见的噪声情况,还没有人对超像素分割算法的鲁棒性进行全面的研究。本文针对常见的噪声类型,研究分析了超像素分割算法的鲁棒性。本文的研究主要分为两个重要阶段。在研究的第一阶段也是最重要的阶段,对最近提出的11种超像素分割算法面对不同类型噪声的鲁棒性进行了性能评估,并在此基础上提出了相应的算法。为此,选取不同程度的二维... 

【文章来源】：山东大学山东省 211工程院校 985工程院校 教育部直属院校

【文章页数】：128 页

【学位级别】：博士

【文章目录】：
Abstract in Chinese
Abstract in English
Chapter 1 Introduction
    1.1 Introduction to Superpixels
    1.2 Background and Motivation
    1.3 Research Overview
    1.4 Primary work and Contribution
    1.5 Outline of the Thesis
Chapter 2 Literature Review
    2.1 A review of commonly used Superpixel Segmentation algorithms
    2.2 Role of Superpixel Segmentation
    2.3 Categories of Superpixel segmentation algorithms
        2.3.1 Clustering based techniques
        2.3.2 Graph-based techniques
        2.3.3 Geometric flow based techniques
    2.4 Conclusion
Chapter 3 Superpixel Segmentation Algorithms
    3.1 Clustering based techniques
        3.1.1 Simple Linear Iterative Clustering (SLIC)
        3.1.2 VCells
        3.1.3 Manifold-SLIC (M-SLIC)
        3.1.4 Real-time superpixel segmentation by a DBSCAN clustering algorithm
        3.1.5 Superpixels Extracted via Energy-Driven Sampling (SEEDS)
    3.2 Graph-based technique
        3.2.1 Lazy Random Walks (LRW)
        3.2.2 Partially absorbing random walks (PARW)
    3.3 Geometric flow based techniques
        3.3.1 Bilateral geodesic algorithm (Bilateral-G)
        3.3.2 Flooding-based superpixel generation (FCCS)
        3.3.3 Structure Sensitive Superpixel via Geodesic distance (SSS-G)
        3.3.4 Turbopixel (TP)
    3.4 Conclusion
Chapter 4 Data set,Benchmarks and Types of Noise
    4.1 Berkeley Segmentation Data-set
    4.2 Evaluation Criteria
        4.2.1 Performance Evaluation Parameters
        4.2.2 Achievable Segmentation Accuracy (ASA)
        4.2.3 Under-segmentation Error (USE)
        4.2.4 Compactness
        4.2.5 Boundary Recall (BR)
    4.3 Types of noise
        4.3.1 2D Gaussian blur
        4.3.2 Additive white Gaussian noise (AWGN)
        4.3.3 Impulse noise
    4.4 Conclusion
Chapter 5 Robustness analysis of Superpixel Segmentation Algorithms
    5.1 Introduction
    5.2 Superpixel Segmentation Algorithms
    5.3 Quantitative Evaluation Measures
    5.4 Experiments and Results
        5.4.1 Evaluation Process
        5.4.2 Experimental Setup
        5.4.3 Experiments on Robustness to 2D-Gaussian Blur
        5.4.4 Experiments on Robustness to Additive White Gaussian Noise(AWGN)
        5.4.5 Experiments on Robustness to Impulse Noise
        5.4.6 Percentage Performance Degradation Analysis
        5.4.7 Rate of Performance Degradation Analysis
    5.5 Conclusion
Chapter 6 Experimental approach for evaluation of superpixels consistency
    6.1 Introduction
    6.2 Problem statement and algorithm Overview
    6.3 Algorithm Details
        6.3.1 Jaccard Similarity Coefficient (JSC)
        6.3.2 Data set and Noise
        6.3.3 Superpixel Segmentation algorithms
    6.4 Our Approach
        6.4.1 Computation of Superpixels
        6.4.2 Computation of similarity indices
        6.4.3 Determination of the coefficient threshold τ
        6.4.4 Algorithm 1
        6.4.5 Final grouping (Output)
    6.5 Experiments and Results
        6.5.1 Experimental Setup
        6.5.2 Experiment One (2D Gaussian Blur)
        6.5.3 Experiment Two (Impulse Noise)
        6.5.4 Experiment Three(2D Gaussian Blur + Impulse Noise)
    6.6 Conclusion
Chapter 7 Conclusion and Future Work
    7.1 Important Observations and Findings
    7.2 List of Recommendations
    7.3 Future Work
References
Acknowledgements
List of Published Papers
学位论文评阅及答辩情况表


【参考文献】：
期刊论文
[1]Saliency detection based on superpixels clustering and stereo disparity[J]. GAO Shan-shan,CHI Jing,LI Li,ZOU Ji-biao,ZHANG Cai-ming.  Applied Mathematics:A Journal of Chinese Universities. 2016(01)
[2]Edge-Weighted Centroidal Voronoi Tessellations[J]. Jie Wang and Xiaoqiang Wang~* Department of Scientific Computing,Florida State University,Tallahassee, FL 532306-4120,USA..  Numerical Mathematics:Theory,Methods and Applications. 2010(02)



本文编号：3474342