結合超廣角拼接與深度取像的雙眼視覺系統設計

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姓名

柯啓強(Chi-Chiang Ko) 查詢紙本館藏

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資訊工程學系

論文名稱

結合超廣角拼接與深度取像的雙眼視覺系統設計
(Design of Binocular Vision System Combining Wide-View Stitching and Depth Imaging)

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摘要(中)

本論文借鏡於不同生物之雙眼視覺型態，期望以仿生方式結合超廣角影像拼接與立體視覺技術設計一種智慧型仿生雙眼視覺系統平台，藉以達到同時進行廣視域範圍監控與深度感測，改善現今許多雙眼機器視覺缺乏靈活性與適應性之問題。我們提出先以K-means演算法處理強化物件深度影像資訊於不同景深平面之特徵，再透過本研究核心演算景深影像接合方法達成任意興趣深度平面之完整影像拼接。經由一系列實驗與SIFT方法驗證，最終證實此系統能成功達成自適應專注聚焦於最為前景深度影像之物件，且該物件於影像拼接重疊區域不會出現分岔錯位之模糊情形。本研究方法結果將能順利結合交融立體視覺與影像拼接系統資訊之特色與優勢，並使得影像拼接系統將由傳統二維平面層次提高至三維立體空間作為研究與探討，以不同以往之角度審視仿生雙眼視覺平台發展之潛力與其未來之可朔性。

摘要(英)

In this paper, we expect to design a smart binocular vision system platform combined ultra-wide-angle image stitching and stereo vision technology after learning binocular visual pattern from different creatures. It can simultaneously implement monitoring in wide field of view and depth sensing and improve the lack of flexibility and adaptability in binocular machine vision nowadays. We propose a method based on K-means algorithm to strengthen object depth information on characteristics of different image planes of depth of field, and achieving full image stitching in any interested depth plane. After a series of experiments and SIFT verification, the system is proved that can successfully implement adaptive focus on the object in the foreground of the image. Meanwhile, the object will not bifurcate or having dislocation in image stitching overlap region. Methods in this research are able to successfully combine the features and advantages of stereo vision blending and image stitching system information, and allow the system to enhance image stitching from the traditional two-dimensional spaces to three-dimensional space for research and discussion. Therefore, we can view the potential development and plasticity of binocular vision platform in a different way from the past.

關鍵字(中)

★ 雙眼視覺
★ 視覺伺服
★ 立體視覺
★ 影像拼接

關鍵字(英)

★ Binocular Vision
★ Visual Servo
★ Stereo Vision
★ Image Stitching
★ K-means
★ SIFT
★ GRAFCET
★ IDEF0

論文目次

摘要 I
Abstract II
誌謝 III
目錄 V
圖目錄 VII
表目錄 XI
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 4
1.3 論文架構 5
第二章文獻回顧 6
2.1 立體視覺 6
2.1.1 立體視覺原理概論 9
2.1.2 Non-Local Cost Aggregation Method 18
2.2 影像拼接 22
2.2.1 影像拼接原理概論 23
2.2.2 MIAT即時連續影像接合系統 34
第三章研究方法與方法論 46
3.1 MIAT系統設計方法論 47
3.1.1 IDEF0 48
3.1.2 GRAFCET 51
3.2 系統架構 54
3.3 影像校正 55
3.4 立體視覺 58
3.5 影像拼接 60
3.6 景深影像接合法 62
3.6.1 K-means影像分群優化 63
3.6.2 縫合線位置估算 66
第四章實驗結果與探討 72
4.1 系統平台建置 72
4.2 系統模組驗證 77
4.3 多視角影像之比較 81
4.4 景深影像接合驗證 85
4.4.1 K-means優化結果 87
4.4.2 縫合線位置估算驗證 89
4.4.3 自適應前景拼接結果 90
4.4.4 SIFT評估拼接結果 92
第五章結論與未來展望 98
5.1 結論 98
5.2 未來展望 99
參考文獻 101

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指導教授

陳慶瀚

審核日期

2014-11-14

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