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研究生:吳佶融
研究生(外文):Ji-Rong Wu
論文名稱:低成本立體視訊之多重缺陷深度修補系統及其評量
論文名稱(外文):Multiple-Flaw Depth Repairing Mechanism and Its Specificity Assessment for Low-cost Stereoscopic videos
指導教授:章定遠
指導教授(外文):Din-Yuen Chan
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:資訊工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:105
語文別:中文
論文頁數:47
中文關鍵詞:立體視訊成像低成本3D攝影深度圖修補影像評估
外文關鍵詞:depth repairingdepth inpaintinglow-cost 3D videosStereoscopic video
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使用低成本攝影機所拍攝的深度圖總是有大範圍缺少深度資料,且存在著嚴重的雜訊,導致深度圖的不穩定。這些缺陷在3D視訊合成影像上,成像的物體因深度值缺乏、破損或跳動,造成視覺感受度不佳。為了增強深度圖的品質與穩定,本文提出了經由Kinect彩圖資訊以及其深度圖,經由時間、空間上的資訊進行無切割式多重缺陷空洞填補,由大範圍時間性修補至小範圍輪廓性修補,以加強深度穩定性。其修補深度圖能夠提供Kinect彩色影像,合成不錯的虛擬視角,完成有品質之立體視訊顯像。為了驗證本文方法有效性,提出了滿足人眼視覺系統與適合低成本視訊品質的評定方式,以SSIM(Structural Similarity)為基礎,對修補後的深度圖進行有效的評估。模擬結果可呈現良好之深度圖和虛擬視角影像,量化的評估也證實實驗的實用性,使得研究對未來的3D成像系統及發展有極大的貢獻與應用。
Depth maps captured by the low-cost sensor are always full noise. On 3D videos, defective depth maps lead to quality inferior. In this letter, we propose an algorithm to repair depth maps. Its framework consists of two repairing procedures. First, non-segmentation hole filling and color edge guiding achieved depth map reconstruction. Second, used two processing to make depth map stability: temporal prediction-based depth-coherence processing and temporal occlusion-excluded edge dilating-to-tailoring. The simulation result demonstrated that the proposed algorithm obtains acceptable depth map and stable quality. In addition, our algorithm can procure firmware realization. For affirming the performance of proposed scheme, competent quantitative metrics are specified to the appropriated experiments. Based on the proposed depth refinement, stereoscopic 3D approach can be attained by low-cost depth sensor photographing.
摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 viii
第一章、 簡介 1
1.1 3D視訊技術相關背景與趨勢 1
1.1.1 3D影像拍攝技術 1
1.1.2 3D影像成像格式 2
1.2 影像品質的評估與測量方法 4
1.3 研究動機與目的 5
1.4 論文架構 5
第二章、 相關研究 7
2.1 Kinect深度攝影機與深度影像繪圖法(Depth Image Based Rendering) 7
2.1.1 Kinect深度攝影機簡介與深度攝影機使用相關研究 7
2.1.2 深度影像繪圖法(Depth Image Based Rendering) 10
2.2 影像品質的評估與測量方法 11
第三章、 低品質連續深度圖修復之研究 14
3.1 Kinect Sensing-Loss Hole(KSLH)判定機制 15
3.2 無需影像切割進行KSLH之修補 16
3.2.1 KSLH之時間性修補 16
3.2.2 KSLH之空間性修補 17
3.3 修補扭曲的變形邊緣 19
3.4 時間上深度一致性 21
3.4.1. 基於時間預測的深度一致性 21
3.4.2. 時間上深度邊緣裁剪一致性 22
3.4.3. 形態學與非線性運算之修補 23
3.5 雙因子濾波器(Bilateral Filter) 24
3.6 Histogram-equalized SSIM與類真深度圖 24
第四章、 實驗結果與討論 27
4.1 修補結果 27
4.2 客觀H-SSIM與主觀定量評估 32
第五章、 結論與未來展望 34
5.1 結論 34
5.2 未來展望 35
參考文獻 36
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