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研究生： 卡齊
Qazi Mazhar Ul Haq
論文名稱： 一種基於邊緣感知的雙目圖像立體匹配特徵提取技術
An Edge­-aware Based Feature set Extraction and Stereo Matching of Binocular Images Under Radiometric Variation
指導教授： 阮聖彰
Shanq-Jang Ruan
口試委員: 林昌鴻
Chang-Hong Lin
呂政修
Jenq-Shiou Leu
魏榮宗
Rong-Jong Wai
彭文志
Wen-Chih Peng
彭彥璁
Yan-Tsung Peng
學位類別： 博士
Doctor
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2021
畢業學年度： 110
語文別： 英文
論文頁數： 90
中文關鍵詞： 立體匹配 、功能集 、直方圖均衡 、双目影像 、梯度模型 、中值濾波
外文關鍵詞： Stereo Matching, Feature sets, Histogram Equalization, Binocular images, Gradient Models, Median filtering
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    • 物件立體視覺通常是電腦視覺中深入研究的領域之一。立體匹配用於許多現代應用，包括機器人導航、增強現實和汽車應用。儘管它有著悠久的研究歷史，但對於輻射變化下的無紋理、不連續和遮擋區域的邊緣仍然具有挑戰性。這篇研究文章提出了一種改進的長條圖均衡化、一種新穎的特徵提取、一種空間梯度模型和匹配成本，它對在不同輻射變化下拍攝的圖像具有魯棒性和穩定性。所提出的方法將不良圖元的平均百分比降低到 3.35，並將 Middlebury 資料集上差異照明和曝光值的相對均方誤差 (RMSE) 降低到 30.08。對所提出方法的定量和定性評估表明，在增加 PSNR 和降低壞圖元百分比方面對輻射變化和最先進的局部立體匹配演算法有顯著改善。

    Object Stereo Vision has conventionally been one of the deeply examined areas in com­puter vision. Stereo matching is employed in numerous modern applications, including robot navigation, augmented reality, and automotive applications. Even though it has a long research history, it is still challenging for the edges of textureless, discontinues, and occluded regions under radiometric variation. This research article proposes a modified histogram equalization, a novel feature extraction, a spatial gradient model, and matching cost, which is robust and stable to images taken in different radiometric variations. The proposed method reduced the average percentage of bad pixels to 3.35 and reduced the relative mean square error (RMSE) up to 30.08 on the Middlebury dataset for different illumination and exposure values. Quantitative and qualitative evaluation of the proposed method demonstrates significant improvement in increasing PSNR and decreasing bad pixel percentage against radiometric variation and state­of­the­art local stereo matching algorithms.

    Recommendation Letter Approval Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abstract in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Symbol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Applications of Stereo Matching . . . . . . . . . . . . . . . . . . . . . . 1.3 Fundamentals of Stereo Matching . . . . . . . . . . . . . . . . . . . . . 1.4 Disparity Estimation steps . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Stereo Matching Under Radiometric Condition . . . . . . . . . . . . . . 1.6 Organization of this Dissertation . . . . . . . . . . . . . . . . . . . . . . 2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Overview of Stereo Matching . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Global methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Local methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Multi­stages of Stereo Matching . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Matching Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Cost Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Disparity Optimization . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 Disparity Map Refinement: . . . . . . . . . . . . . . . . . . . . . 2.5 Existing techniques on Stereo Matching . . . . . . . . . . . . . . . . . . 2.5.1 Sum of Square Differences (SSD): . . . . . . . . . . . . . . . . . 2.5.2 Sum of Absolute Differences (SAD): . . . . . . . . . . . . . . . 2.5.3 Census Transform (CT): . . . . . . . . . . . . . . . . . . . . . . 2.5.4 Normalized Cross Correlation (NCC): . . . . . . . . . . . . . . . 2.5.5 Rank Transform (RT): . . . . . . . . . . . . . . . . . . . . . . . 3 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Modified histogram equalization . . . . . . . . . . . . . . . . . . . . . . 3.2 Extracting Feature Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Adaptive Entropy based Weighting . . . . . . . . . . . . . . . . . . . . . 3.4 Local Stereo Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Weighted Median Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Evaluation of Robustness to Radiometric Variation . . . . . . . . . . . . 4.3 Evaluation and Comparisons on Percentage of Bad Pixels. . . . . . . . . 4.4 Evaluation on Peak Signal to Noise Ratio (PSNR) . . . . . . . . . . . . . 4.5 Root Mean Square Value (RMSE) . . . . . . . . . . . . . . . . . . . . . 4.6 Visualization of Images for Different Radiometric Condition . . . . . . . 5 Conclusions and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . APPENDIX A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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