本論文整合了數個獨立並已發展成熟之系統，用以測試高光消除對於立體影像比對是否能有正面之影響。利用一左右成對之立體影像，經過計算後重建影像之深度資訊乃現行以立體影像擷取設備獲得場景景深的主流方法。但在立體影像比對的過程中，我們發現影像之高反光部分可能為降低立體影像比對準確度的一個重要因素。為此我們結合了包括高光消除，未經相機校準之影像矯正，以及立體影像比對等數個已發展成熟的演算法，並將其結合成一套可測試高光消除能否增進立體影像比對準確度之系統。我們將輸入的影像分為兩組，其中一組消去高光元件，一組則維持原樣，並將這兩組測試影像資料分別進行傳統之立體影像比對流程，即經過影像矯正後再予以立體影像比對，最後將兩組經由我們的系統產生之場景深度圖直接進行比較。經由實驗證明，高光消除對於立體影像比對的準確度的確有著顯著的正面支持作用，其影像深度圖正確度之差異無須經由專業之分析即可一眼明辨，一如我們的論點所述。

1. S.A. Shafer, "Using Color to Separate Reflection Components", COLOR Research and application, 10(4):210-218 (1985)

2. H.-C. Lee, E.J. Breneman, and C.P. Schulte, “Modeling Light Reflection for Computer Color Vision”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 4, pp. 402-409, 1986Analysis and Machine Intelligence, vol. 12, no. 4, pp. 402-409 (1986)

3. S. Nayar, X.-S. Fang, and T. Boult, “Separation of reflection components using color and polarization”, IJCV 21, 3 163–186 (1997)

4. P. Tan, S. Lin, L. Quan, and H.-Y Shum, “Highlight Removal by Illumination-Constrained Inpainting”, In Ninth IEEE International Conference on Computer Vision. (2003)

5. P. Tan, S. Lin, and L. Quan, “Separation of Highlight Reflections on Textured Surfaces”, Proceedings of IEEE conference on computer vision and pattern recognition (2006)

6. R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image”, PAMI, 27(2):178-193 (2005)

7. Q. Yang, K. -H. Tan, and N. Ahuja. “Real-time o(1) bilateral filtering”, In CVPR, pages 557–564, (2009)

8. Q. Yang, S. Wang, and N. Ahuja, ”Real-time Specular Highlight Removal Using Bilateral Filtering”, In ECCV (2010)

9. Z. Zhang, “A flexible new technique for camera calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334 (2000)

10. P. Mittrapiyanuruk, “A Memo on How to Use the Levenberg-Marquardt Algorithm for Refining Camera Calibration Parameters”.
http://cobweb.ecn.purdue.edu/~kak/courses-i-teach/ECE661/HW5_LM_handout.pdf

11. J. Y. Bouguet, “3D transformations & camera calibration”, Technical Report, California Institute of Technology, Pasadena. (1998)

12. R. A. Lane, and N. A. Thacker, “Tutorial: Overview of Stereo Matching Research”, Imaging Science and Biomedical Engineering Division, Medical School, University of Manchester, M13 9PT, pp. 1-10 .(1998)

13. A. Fusiello, E. Trucco, and A. Verri, “A compact algorithm for rectification of stereo pairs”, Machine Vision Appl. 12 (1) 16–22. (2000)
14. D. Lowe. “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 2(60):91-110 (2004)

15. S. T. Barnard, "Stereo Matching by Hierarchical Microcanonical Annealing," Tenth International Joint Conference on Artificial Intelligence, Milan, Italy, August, 1987, pp. 832-835. (1987)

16. S. N. Lim, A. Mittal, L.S. Davis, and N. Paragios, “Uncalibrated Stereo Rectification for Automatic 3D surveillance,” ICIP Vol. 3, pp. 369 – 372 (2004)

17. J. P. Lewis, “Fast Template Matching”, Vision Interface, p. 120-123 (1995)

18. G. Medioni, and R. Nevatia, “Segmented-Based Stereo Matching”, Computer Vision, Graphics and Image Processing 31, 2-18 (1985)

19. R. Ma, M. Thonnat, and M. Berthod, “An adjustment-free stereo matching algorithm”, In J. Illingworth, editor, Proc. British Machine Vision Conf., pages 609-618 (1993)

20. S. Paris, ““Fixing the Gaussian Blur”: the Bilateral Filter -- A Gentle Introduction to Bilateral Filtering and its Applications”, Adobe

21. Siddhant Ahuja’s website -- http://siddhantahuja.wordpress.com/

22. Camera calibration toolbox for Matlab -- http://www.vision.caltech.edu/bouguetj/calib_doc/