在過去幾年中,神經(jīng)生物學中的顯著性檢測成為一項重要研究。在處理圖像時,由于存在一些人類興趣區(qū)域（ROI）等重要數(shù)據(jù),人們使用不同注意力水平感知圖像信息。視覺顯著性區(qū)域檢測是一種實現(xiàn)圖像ROI的有效方式。顯著性被定義為“一種專注于圖像中最有價值部分的注意機制。一些認知和交互系統(tǒng)用來模擬顯著性模型。盡管目前存在各種用于顯著性檢測的先進算法,但相對于無限制和復雜場景中時間成本計算和顯著對象分割,在性能改進方面仍具有挑戰(zhàn)性。本文重點研究了圖像中視覺顯著性區(qū)域的檢測與融合方法。顯著性檢測在圖像視頻壓縮、目標識別、圖像編輯、圖像縮略圖創(chuàng)建,圖片拼貼,圖像重定向和圖像檢索等不同圖像處理問題中有廣泛的應用。本文主要基于視覺顯著性檢測,在自然圖像中使用自底向上的方法進行重要顯著物體的檢測和分割。在本文中,我們研究了三種新型的自底向上的顯著性檢測方法,以解決目前顯著性檢測算法存在的問題。首先,在第一章中我們對顯著性檢測算法在不同圖像處理問題中的應用進行了概述。第二章簡要介紹了現(xiàn)有的自底向上的視覺顯著性檢測方法。接下來,我們提出了兩種創(chuàng)新的自底向上的顯著性檢測算法以更好地估計突出目標,以及一種新的圖像顯著區(qū)... 

【文章來源】：大連理工大學遼寧省 211工程院校 985工程院校 教育部直屬院校

【文章頁數(shù)】：105 頁

【學位級別】：博士

【文章目錄】：
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



本文編號：3500001