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研究生:洪偉豪
研究生(外文):HONG, WEI-HAO
論文名稱:使用開放計算語言設計之以ORB技術為基礎的影像縫合方法
論文名稱(外文):Design of an ORB-based Image Stitching Method by Open Computing Language
指導教授:連志原
指導教授(外文):Lien, Chih-Yuan
口試委員:陳培殷劉炳宏
口試委員(外文):Chen, Pei-YinLiu, Bing-Hong
口試日期:2018-07-19
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:44
中文關鍵詞:特徵點ORB影像縫合OpenCL嵌入式
外文關鍵詞:feature pointsORBOpenCLimage stitchingembedded platform
相關次數:
  • 被引用被引用:0
  • 點閱點閱:163
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  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
近幾年來,影像縫合技術一直都是電腦視覺領域中廣受重視的議題。在行車輔助系統、監視系統、運動攝影機等應用中,都需要即時影像縫合技術。其中ORB是一套在影像縫合技術中用來偵測並描述影像的區域特徵演算法,其演算法包含以下兩個步驟:(1) Oriented FAST、(2) Rotated BRIEF。接著我們透過RANSAC (Random sample consensus) 將特徵點進行匹配及修正,由於兩張影像為不同平面,因此我們透過圓柱投影將影像投影至相同平面,最後將重疊區塊進行多頻段混合(Multi-Band Blending),消除影像差異將兩張影像進行縫合。接著考量OpenCL框架的平行運算與分工並導入嵌入式平台,將原本的縫合技術進行改良、任務分割與加速,最後實現出一個應用於嵌入式平台且使用開放計算語言設計之以ORB技術為基礎的影像縫合方法。
In recent years, image stitching has been widely discussed in computer vision. In the applications of driver assistant and surveillance systems, image stitching is one of the most important components. Feature extraction and classification are two main steps in image stitching. ORB is an algorithm used in computer vision to detect and describe images. The algorithm consists of the following two steps: (1) Oriented FAST and (2) Rotated BRIEF. Next, we match and adapt the feature points through RANSAC (random sample consensus), then project the different images into the same flat by cylindrical projection. Finally, multi-band blending is used to deal with the overlap of images to eliminate the difference between images. Consider the parallel computing and heterogeneous computing of OpenCL on the various embedded platforms, we can improve and accelerate our image stitching method. Finally, an ORB-based Image Stitching Method by Open Computing Language for the embedded platform is proposed.
目錄

第一章、 緒論
1.1. 研究背景
1.2. 研究動機
1.3. 研究方向
1.4. 論文組織
第二章、 相關文獻探討
2.1. SIFT: Scale-invariant feature transform
2.2. SURF: Speeded Up Robust Features
2.3. ORB: Oriented FAST and Rotated BRIEF
2.3.1. 特徵檢測FAST corner detection
2.3.2. 特徵檢測Harris corner detection
2.3.3. 特徵點角度檢測
2.3.4. 特徵點描述
2.3.5. 特徵點匹配
2.4. Perspective Transform and RANSAC
2.4.1. 透視矩陣
2.4.2. 隨機抽樣RANSAC
2.4.3. Alpha Blend
2.5. OpenCL (Open Computing Language)
2.5.1. GPU並行運算的優勢
2.5.2. 執行流程
2.5.3. 記憶體模型(Memory model)
2.5.4. 編程模型(Programming model)
2.5.5. 平行程式設計模型
2.6. NVIDIA Jetson TX2
第三章、 論文提出的方法
3.1. Harris角點檢測
3.2. 圓柱投影
3.3. 拉普拉斯金字塔混合
第四章、 實驗結果
4.1. ORB與SIFT縫合圖
4.2. 縫合影像結果
4.3. 各個尺度測試結果
第五章、 結論與未來工作
5.1. 結論
5.2. 未來工作
參考文獻


[1].C. Harris and M. Stephens, “A combined corner and edge detector,” in Alvey Vision Conf., pp. 147–151, 1988.
[2].J. Shi and C. Tomasi, “Good features to track,” in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 593–600, IEEE Computer Society Press, 1994.
[3].M. Trajkovic and M. Hedley, “FAST corner detection,” Image and Vision Computing, 16 (1998) 75–87
[4].E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proc. IEEE Int. Conf. Comput. Vis., 2011, vol. 13, pp. 2564–2571.
[5].D. Lowe, “Distinctive image features from scale-invariant keypoints,” int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.
[6].H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Comput. Vis. Image Understanding, vol. 110, pp. 346– 359, 2008.
[7].M. Calonder, V. Lepetit, C. Strecha and P. Fua, “Brief: Binary robust independent elementary features,” in European Conference on Computer Vision, 2010. 1, 2, 3, 5
[8].G. Levi and T. Hassner, “LATCH: learned arrangements of three patch codes,” in Winter Conference on Applications of Computer Vision (WACV), 2016.
[9].M. Fischler and R. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, 24:381–385, 1981.
[10]. B. Gaster, L. Howes, D. R. Kaeli, P. Mistry, and D. Schaa, “Heterogeneous Computing with OpenCL: Revised OpenCL 1.2 Edition,” Morgan Kaufmann, 2012.
[11]. Shum H. Y. and Szeliski, R, “Construction of panoramic image mosaics with global and local alignment,” Int. Journal of Computer Vision, 16(1):63–84, 2001
[12]. Burt, P. Adelson, E. “A multiresolution spline with application to image mosaics,” ACM Trans. on Graphics, 2,1983, pp. 217-236
[13]. 顏瑞穎, “Research and Implementation for an Image Stitching Method Based on OpenCL,” 碩士論文, 高雄應用科技大學, 2017
[14]. Bo-Sang Kim, Kang-A Choi and Won-Jae Park, “Content-Preserving Video Stitching Method for Multi-Camera Systems,” IEEE Transactions on Consumer Electronics, vol. 63, no. 2, May 2017
[15]. 任剛, 彭冬亮, 穀雨, “基於圓柱面映射的快速圖像拼接演算法,” [J]. 電腦應用研究 2017, 34 (11): 1-8.
[16]. Y. W. Huang, J. G. Xu. (2015). http://kkyang.github.io/IMGSTCHHW2
[17]. Z. X. Ding “A Study on Video Stitching Using the SURF,” 2017
[18]. M. Uyttendaele, A. Eden, and R. Szeliski. “Eliminating ghosting and exposure artifacts in image mosaics,” in Proceedings of the Interational Conference on Computer Vision and Pattern Recognition, volume 2, pages 509–516, Kauai, Hawaii, December 2001.
[19]. G. Levi and T. Hassner, “LATCH: learned arrangements of three patch codes,” in Winter Conference on Applications of Computer Vision (WACV), 2016.
[20]. C. Parker et al. “The CUDA LATCH binary descriptor: because sometimes FASTer means better,” in European Conf. on Computer Vision (ECCV), Amsterdam, The Netherlands (2016)
[21]. 萬鴻政, “以內向式雙鏡頭取像之影像縫合技術,” 碩士論文, 國立交通大學, 2010
[22]. H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,“ in European Conference on Computer Vision, May 2006

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