臺灣博碩士論文加值系統

本論文是利用環景接圖的技術在HoloAD顯示平台上顯示出Holography影像，而特別針對在輸入影像前處理的部份做色彩校正的研究，接著找出輸入影像的特徵點並互相匹配，最後合成出一張全景圖，然後顯示在HoloAD平台上，而色彩校正會使環景接圖後的影像更加自然，顯示Holography影像的效果能更明顯，之後可針對使用者不同的需求來完成實現不同的系統，像是可當成展示商品的顯示器，讓觀賞者可以看到商品360度的全貌，另外還可結合互動的功能，達到更好的效果。
在硬體實現上，針對演算法中所提出基於全景接圖色彩校正的部份實現成晶片，且整體架構採Low Power的設計，可以節省25%的功耗，所實現的晶片非常適合應用在行動裝置上，延長其使用時間。

This thesis uses panorama technology to display a holography images on the HoloAD platform. We adopt color correction method to perform image pre-processing and then find the feature points of the input image and match with each other. Finally, images will be blent a panoramic image and then be displayed on HoloAD platform. Color correction skill makes panorama appearing a more natural display holography image. It can be more obvious after the user may be different requirements for completion of the implementation, such as a monitor can display of goods, so that the user can see a 360-degree panorama of goods, in addition to interactive features can be combined to achieve better results.
On the hardware implementation for the proposed algorithm in color correction of panorama implemented as part of the chip, and the overall architecture adopts low power design, it can save 25% of power consumption, implement very suitable for applications in chip on mobile devices to extend their use of time.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 ix
第一章 介紹 1
1.1 簡介 1
1.2 發展現況與重要性 2
1.2.1 眼鏡式立體顯示技術 2
1.2.2 裸眼立體顯示技術 5
1.3 研究動機 8
1.4 論文章節組成 9
第二章 文獻回顧與探討 10
2.1 前處理相關技術 11
2.2 影像匹配相關技術 15
2.3 影像合成相關技術 16
2.4 結論 18
第三章 研究方法 19
3.1 系統架構 19
3.2 影像前處理 20
3.2.1 色彩空間轉換 20
3.2.2 直方圖等化與色調曲線調整 21
3.2.3 色彩補償 24
3.3 影像間的匹配 27
3.3.1 SURF 27
3.3.2 K-NN演算法 32
3.3.3 RANSAC 32
3.4 影像間的合成 33
3.4.1 影像間的調整 33
3.4.2 影像間的拼接 34
3.5 Holography系統顯示 35
3.6 結論 37
第四章 硬體架構與實現 38
4.1系統架構 38
4.2 RGB色彩空間轉HSI色彩空間電路 40
4.3 直方圖等化電路 41
4.4 色調曲線調整電路 42
4.5 色彩補償電路 45
4.6 HSI色彩空間轉RGB色彩空間電路 45
4.7 結論 48
第五章 實驗方法與結果 50
5.1 全景接圖影像品質模擬方法 53
5.2 實驗結果 56
5.3 客觀方法比較 80
5.3.1 偏移量SAD值 80
5.3.2 接圖配對數 83
5.3.3 峰值噪訊比 85
5.3.4 結構相似性 88
5.4 主觀方法比較 93
5.5 結論 98
第六章 晶片設計流程 99
6.1 數位電路設計流程 99
6.1.1電路規格 100
6.1.2暫存器轉換層級設計 102
6.1.3邏輯合成 102
6.1.4可測試性電路設計 105
6.1.5電路實體層級設計 106
6.1.6 實體驗證 107
6.1.7 Post-layout Simulation 108
6.2 晶片實體佈局圖及規格 108
6.3數位晶片量測流程 112
6.4結論 115
第七章 總結與未來展望 116
7.1 總結 116
7.2 未來展望 117
參考文獻 118
附錄A 發表論文 122
附錄B 測試報告 123

[1]3D顯示器市場預測, http://hollywoodinhidef.com/2010/01/22-bil-3d-market-in-2018/
[2]偏光式眼鏡動作原理, http://science.howstuffworks.com/3-d-glasses2.htm
[3]快門式眼鏡動作原理, http://www.hardwarezone.com.sg/feature-hardwarezones-3d-tv-buying-guide-essentials/understanding-3d-tv-technology
[4]空間多工式立體顯示技術示意圖, http://www.3d-forums.com
[5]體積式立體顯示技術, http://www.dgp.toronto.edu/~gf/Research/Volumetric%20UI/volumetricResearch.htm
[6]Y. Shimozato, P. Xia, T. Tahara, T. Kakue Y. Awatsuji, K. Nishio, S. Ura, T. Kubota and O. Matoba, “Four-wavelength Color Digital Holography,” IEEE Journal of Display Technology, vol. 8, no. 10, pp. 570-576, October, 2012.
[7]V. M. Bove, “Display Holography''s Digital Second Act, ” Proceedings of the IEEE, vol. 100, no. 4, pp. 918-928, April, 2012.
[8]S. Liu, L. Zhao, J. Li, and Q. Cai, “The Applications and Summary of Panoramic Modeling, ” in International Conference on Industrial Control and Electronics Engineering (ICICEE), 2012, pp. 628-631.
[9]A. Eden, M. Uyttendaele, and R. Szeliski, “Seamless Image Stitching of Scenes with Large Motions and Exposure Differences. ” in IEEE Computer Vision and Pattern Recognition, 2006, pp. 2498-2505.
[10]J. Lin, “An Automatic White Balance Method based on Edge Detection. ” in IEEE International Symposium on Consumer Electronic (ISCE), 2006, pp. 1-4.
[11]S. Kim, W. J. Kim, and S. D. Kim, “Automatic White Balance based on Adaptive Feature Selection with Standard Illuminants. “ in IEEE International Conference on Image Processing (ICIP), 2008, pp. 485-488.
[12]J. Duan, W. Dong, R. Mu, and G. Qiu, “Local Contrast Stretch based Tone Mapping for High Dynamic Range Images. ” in IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), 2011, pp. 26-32.
[13]Y. Xiong and K. Pulli, “Color Matching for High-quality Panoramic Images on Mobile Phones, ” IEEE Transactions on Consumer Electronics, November, 2010, pp.2592-2600.
[14]D. G. Lowe, “Object Recognition from Local Scale-invariant Features, ” IEEE International Conference on Computer Vision, 1999, pp.1150-1157.
[15]H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool “SURF: Speeded Up Robust Features, ” Computer Vision and Image Understanding (CVIU), vol. 110, no. 3, pp. 346-359, September, 2008.
[16]S. Ali and M. Hussain “Panoramic Image Construction Using Feature based Registration Methods, ” International Multitopic Conference (INMIC), 2012, pp. 209-214.
[17]Y. Xiong, “Fast Image Stitching and Editing for Panorama Painting on Mobile Phones, ” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010, pp.13-18.
[18]J. H. Cha, Y. S. Jeon, Y. S. Moon, and S. H. Lee, “Seamless and Fast Panoramic Image Stitching,” IEEE International Conference on Consumer Electronics (ICCE), 2012, pp. 29-30.
[19]R. Gonzalez, R. Woods and S. Eddins, “Digital Image Processing Using MATLAB,” Pearson Prentice Hall, Upper Saddle River, 2004.
[20]CIE色度圖, http://homepage.ntust.edu.tw/bridge/
[21]G. Y. Tian, D. Gledhill, D. Taylor and D. Clarke, “Colour Correction for Panoramic Imaging,” International Conference on Information Visualisation, 2002, pp.483-488.
[22]S. J. Ha, H. I. Koo, S. H. Lee, N. I. Cho and S. K. Kim, “Panorama Mosaic Optimization for Mobile Camera Systems,” IEEE Transactions on Consumer Electronics, 2007, pp.1217-1225.
[23]M. Brown and D. Lowe, “Automatic Panoramic Image Stitching Using Invariant Features, ” in International Journal of Computer Vision, vol. 74, no. 1, pp. 59-77, 2007.
[24]Y. Xiong and K. Pulli, “Color Matching for High-quality Panoramic Images on Mobile Phones, ” IEEE Transactions on Consumer Electronics, November, 2010, pp.2592-2600.
[25]維基百科峰值噪訊比, http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
[26]Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
[27]k. Kokufuta, T. Maruyama, “Real-time Processing of Local Contrast Enhancement on FPGA,” International Conference on Field Programmable Logic and Applications, 2009, pp.288-293.
[28]Y. P. Hsu, H. C. Miao and C. C. Tsai, “FPGA Implementation of a Real-time Image Tracking System,” Proceedings of SICE Annual Conference, 2010, pp. 2878-2884.
[29]M. C. Hanumantharaju, M. Ravishankar, D. R. Rameshbabu and S. Ramachandran, “A Novel FPGA Implementation of Adaptive Color Image Enhancement Based on HSV Color Space,” International Conference on Electronics Computer Technology(ICECT), 2011, pp. 160-163.
[30]Agilent Technologies, “Agilent 93000 SOC Series User Training Part 1” Oct. 2004.