图像中视觉显著区域的检测与融合

发布时间：2021-11-17 01:55

　　在过去几年中,神经生物学中的显著性检测成为一项重要研究。在处理图像时,由于存在一些人类兴趣区域（ROI）等重要数据,人们使用不同注意力水平感知图像信息。视觉显著性区域检测是一种实现图像ROI的有效方式。显著性被定义为“一种专注于图像中最有价值部分的注意机制。一些认知和交互系统用来模拟显著性模型。尽管目前存在各种用于显著性检测的先进算法,但相对于无限制和复杂场景中时间成本计算和显著对象分割,在性能改进方面仍具有挑战性。本文重点研究了图像中视觉显著性区域的检测与融合方法。显着性检测在图像视频压缩、目标识别、图像编辑、图像缩略图创建,图片拼贴,图像重定向和图像检索等不同图像处理问题中有广泛的应用。本文主要基于视觉显著性检测,在自然图像中使用自底向上的方法进行重要显著物体的检测和分割。在本文中,我们研究了三种新型的自底向上的显著性检测方法,以解决目前显著性检测算法存在的问题。首先,在第一章中我们对显著性检测算法在不同图像处理问题中的应用进行了概述。第二章简要介绍了现有的自底向上的视觉显著性检测方法。接下来,我们提出了两种创新的自底向上的显著性检测算法以更好地估计突出目标,以及一种新的图像显著区... 

【文章来源】：大连理工大学辽宁省 211工程院校 985工程院校 教育部直属院校

【文章页数】：105 页

【学位级别】：博士

【文章目录】：
Abstract
摘要
1 General Introduction
    1.1 Background
    1.2 Application of Saliency Detection Algorithms in Different Image ProcessingProblems
        1.2.1 Saliency Based Facial Features Detection
        1.2.2 Saliency Based Image and Video Segmentation
        1.2.3 Saliency Based Image Cropping
        1.2.4 Saliency Based Image and Video Compression
    1.3 Scope
    1.4 Contributions
    1.5 Dissertation Organization
2 Literature Review, Motivations and Evaluation Matrices
    2.1 Introduction
    2.2 Overview of Previous Salient Region Detection Methods
    2.3 Addressed Problems and Motivations
    2.4 Evaluation Matrices and datasets
        2.4.1 Datasets
        2.4.2 Evaluation Matrices
    2.5 Conclusion
3 CFBF-SRD:Color Frequency Features and Bayesian Framework Based Salient RegionDetection
    3.1 Introduction
    3.2 Formulation and Related Work of CFBF-SRD
        3.2.1 Log-Gabor Filter
        3.2.2 Bayesian Framework for Saliency
    3.3 Main Steps of CFBF-SRD
    3.4 Experimental Classification Results and Analysis
        3.4.1 Dataset and Parameter Settings
        3.4.2 Graphical Representation
        3.4.3 Computational Time Cost
        3.4.4 Segmentation by Adaptive Thresholding
        3.4.5 Failure Cases
    3.5 Discussion
    3.6 Conclusion
4 SAMM-SRD:Surroundedness and Absorption Markov Model Based Salient RegionDetection
    4.1 Introduction
    4.2 Related Work
        4.2.1 Surroundedness based Eye Fixation Prediction
        4.2.2 The Absorption Markov Chain: Review
    4.3 Proposed SAMM-SRD Algorithm
        4.3.1 Eye Fixation Prediction
        4.3.2 Graph Model Construction
        4.3.3 Construct Transfer Matrix
        4.3.4 Detect initial Saliency Map S_1
        4.3.5 Detect Initial Saliency Map S_2
        4.3.6 Fusion
        4.3.7 Smoothing
    4.4 Experiments
        4.4.1 Evaluation of Experimental Results
        4.4.2 Computational Time Cost
        4.4.3 Adaptive Thresholding based Segmentation
        4.4.4 Comparison
    4.5 Conclusion
5 DSET-SRF: DS-Evidence Theory Based Salient Regions Fusion
    5.1 Introduction
    5.2 Related Work
    5.3 Proposed DSET-SRF Algorithm
        5.3.1 DS-Evidence Theory:Review
        5.3.2 Main Steps of DSET-SRF Algorithm
    5.4 Experiments and Results
        5.4.1 Data-Sets
        5.4.2 Evaluation Metrics
        5.4.3 Performance Comparison
    5.5 Conclusions
6 Summary and Future Work
    6.1 Introduction
    6.2 Summary
    6.3 Future Work
Abstract of Innovation Points
References
Publications and Research Achievements During Ph.D. Period
Acknowledgement
About the Author



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