結合人眼萃取模型之主動式車用 遮陽板之研究

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姓名

楊宗翰(Zong-Han Yang) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

結合人眼萃取模型之主動式車用遮陽板之研究
(Study on active vehicle sun visors incorporated with human-eye extraction model)

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至系統瀏覽論文 (2027-8-30以後開放)

摘要(中)

本論文設計了一套可應用於車載之防眩光系統，且考慮車載之實時應用我們提出了低演算成本之方法實現此系統，而本系統為模擬駕駛駕車時之情形，使用液晶面板、投射燈、攝影機及人眼模型等進行實驗。而我們將投射燈作為眩光來源並調製液晶局部之穿透率，並以攝影機拍攝人眼模型進行分析，使液晶面板能保護人眼模型不被眩光，而本系統可針對投射燈進行動態防護，且當眩光源消失時，系統也能偵測人眼模型中瞳孔與虹膜之區域眩光點消失，並停止防護之動作。
在本論文中考慮了此系統應用於真實人眼之情形，因此建立了人眼萃取模型，且此模型可精準的萃取人眼中虹膜與瞳孔之區域，之後分析若可基於人眼萃取模型結合更具穩健性之眩光判別演算法，可實現應用於車載之實時防眩光系統。

摘要(英)

This paper designs an anti-glare system for automotive applications. Considering the real-time use in vehicles, we propose a low computational cost method to implement this system. The system simulates driving conditions, using an LCD panel, a projector lamp, a camera, and an eye model for experiments. We use the projector lamp as the glare source and modulate the local transmittance of the LCD. The camera captures the eye model for analysis, allowing the LCD panel to protect the eye model from glare. The system dynamically protects against the projector lamp, and when the glare source disappears, it detects the disappearance of glare spots in the pupil and iris regions of the eye model and stops the protection action.

In this paper, considering the application of this system to real human eyes, an eye extraction model was established. This model can accurately extract the iris and pupil regions of the eyes. Further analysis shows that combining this eye extraction model with a more robust glare detection algorithm can realize a real-time anti-glare system for automotive use.

關鍵字(中)

★ 眩光防護
★ 機器學習
★ 影像辨識
★ 液晶顯示器

關鍵字(英)

★ Glare protection
★ Machine learning
★ Image recognition
★ Liquid crystal display

論文目次

摘要 I
Abstract VI
目錄 VII
圖目錄 X
表目錄 XIV
第一章緒論 1
1-1 引言 1
1-2 研究動機 2
第二章防眩光系統之原理與應用 7
2-1 機器學習 7
2-1-1 自適應增強 8
2-2 電腦視覺 10
2-2-1 積分圖 11
2-3 影像金字塔及影像特徵 14
2-3-1 哈爾小波轉換 17
2-3-2 哈爾特徵 18
2-3-3 哈爾特徵之檢測 21
2-4 形態學影像辨識 25
2-4-1 腐蝕與膨脹 25
2-4-2 開運算與閉運算 29
2-5 影像輪廓辨識 30
2-5-1 坎尼邊緣檢測 30
2-5-2 霍夫轉換檢測 32
2-6 液晶調變 33
第三章 35
3-1 人眼萃取模型之演算法 35
3-1-1 人臉眼部辨識 35
3-1-2 瞳孔與虹膜辨識(1) 39
3-1-3 瞳孔與虹膜辨識(2) 44
3-2 眩光點判別之演算法 49
3-3 液晶面板調控系統 52
第四章演算法與防眩光系統性能分析 56
4-1 演算法之結合 56
4-2 人眼萃取模型之性能分析 57
4-3 眩光點判別演算法之性能分析 61
4-3-1 眩光點演算法之分析結果 63
4-4 防眩光系統之原理與分析 68
4-4-1 防眩光系統性能分析 72
第五章結論與研究展望 83
5-1 結論 83
5-2 研究建議及展望 84
參考文獻 85
中英名詞對照表 90

參考文獻

1. 笪琦、李向東、王如德，一種車輛前風檔玻璃自動防炫目系統，China Patent C103273826A (4 September 2013).
2. 張曉東、馬俊、陳克，一種不影響駕駛員視野的汽車擋風玻璃防炫目裝置，China Patent CN210309868U (14 April 2020).
3. G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, “Machine learning and the physical sciences,” Rev. Mod. Phys. 91, 045002 (2019)
4. M. I. Jordan, and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science 349, 255-260 (2015).
5. B. Mahesh, “Machine learning algorithms-a review,” Int. J. Sci. Res. 9, 381-386 (2020).
6. E. Alpaydin, Machine learning, (MIT press, London, 2021).
7. Y. Freund, and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci. 55, 119-139 (1997).
8. D. D. Margineantu, and T. G. Dietterich, “Pruning adaptive boosting,” presented at ICML, Nashville, USA, 211-218 July 1997.
9. A. J. Ferreira, and M. A. Figueiredo, Ensemble machine learning: Methods and Applications, (Springer, New York, 2012).
10. Y. Sun, Z. Liu, S. Todorovic, and J. Li, “Adaptive boosting for SAR automatic target recognition,” IEEE Trans. Aerosp. Electron. Syst. 43, 112-125 (2007).
11. G. Stockman, and L. G. Shapiro, Computer vision (Prentice Hall PTR, New Jersey, 2001).
12. A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. Intell. Neurosci. 2018, 7068349 (2018).
13. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031-1044 (2010).
14. V. Wiley, and T. Lucas, “Computer vision and image processing: a paper review,” Int. J. Artif. Intell. Res. 2, 29-36 (2018).
15. F. C. Crow, “Summed-area tables for texture mapping,” presented at Proceedings of the 11th annual conference on Computer graphics and interactive techniques, Minnesota, USA, 207-212 July 1984.
16. J. Hensley, T. Scheuermann, G. Coombe, M. Singh, and A. Lastra, “Fast summed-area table generation and its applications,” Comput. Graph. Forum 24, 547-556 2005.
17. E. Tapia, “A note on the computation of high-dimensional integral images,” Pattern Recognit. Lett. 32, 197-201 (2011).
18. J. Díaz, P. P. Vazquez, I. Navazo, and F. Duguet, “Real-time ambient occlusion and halos with summed area tables,” Comput. Graphics 34, 337-350 (2010).
19. D. Nehab, A. Maximo, R. S. Lima, and H. Hoppe, “GPU-efficient recursive filtering and summed-area tables,” ACM Transactions on Graphics (TOG) 30, 1-12 (2011).
20. E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA engineer 29, 33-41 (1984).
21. Y. Pang, T. Wang, R. M. Anwer, F. S. Khan, and L. Shao, “Efficient featurized image pyramid network for single shot detector,” presented at Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, California, USA, 7336-7344 June 2019.
22. Y. Mei, Y. Fan, Y. Zhang, J. Yu, Y. Zhou, D. Liu, Y. Fu, T. S. Huang, and H. Shi, “Pyramid attention network for image restoration,” Int. J. Comput. Vision 131, 3207-3225 (2023).
23. L. Xiao, B. Wu, and Y. Hu, “Surface defect detection using image pyramid,” IEEE Sens. J. 20, 7181-7188 (2020).
24. X. Fu, B. Liang, Y. Huang, X. Ding, and J. Paisley, “Lightweight pyramid networks for image deraining,” IEEE Trans. Neural Networks Learn. Syst. 31, 1794-1807 (2019).
25. A. Haar, “Der Massbegriff in der Theorie der kontinuierlichen Gruppen,” Ann. Math. 34, 147-169 (1933).
26. P. Viola, and M. Jones, “Rapid object detection using a boosted cascade of simple
features,” presented at Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, Hawaii, USA, I-I December 2001.
27. J. Wu, S. C. Brubaker, M. D. Mullin, and J. M. Rehg, “Fast asymmetric learning for cascade face detection,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 369-382 (2008).
28. M. T. Pham, Y. Gao, V. D. D. Hoang, and T. J. Cham, “Fast polygonal integration and its application in extending haar-like features to improve object detection,” presented at 2010 IEEE computer society conference on computer vision and pattern recognition, San Francisco, USA, 942-949 June 2010.
29. S. Guennouni, A. Ahaitouf, and A. Mansouri, “A Comparative Study of Multiple Object Detection Using Haar‐Like Feature Selection and Local Binary Patterns in Several Platforms,” Modell. Simul. Eng. 2015, 948960 (2015).
30. C. C. Hsieh, and D. H. Liou, “Novel Haar features for real-time hand gesture recognition using SVM,” J. Real-Time Image Process. 10, 357-370 (2015).
31. Y. N. Lin, T. Y. Hsieh, J. J. Huang, C. Y. Yang, V. R. Shen, and H. H. Bui, “Fast Iris localization using Haar-like features and AdaBoost algorithm,” Multimedia Tools Appl. 79, 34339-34362 (2020).
32. P. Soille, Morphological image analysis: principles and applications, (Springer, New York, 1999).
33. F. Zana, and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process. 10, 1010-1019 (2001).
34. J. L. Vivero-Escoto, Y. D. Chiang, K. C. Wu, and Y. Yamauchi, “Recent progress in mesoporous titania materials: adjusting morphology for innovative applications,” Sci. Technol. Adv. Mater. 13, 013003 (2012).
35. D. Keysers, T. Deselaers, C. Gollan, and H. Ney, “Deformation Models for Image Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 1422-1435 (2007).
36. H. Wang, G. Li, Z. Ma, and X. Li, “Application of neural networks to image recognition of plant diseases,” presented at 2012 International conference on systems and informatics, Yantai, China, 2159-2164 May 2012.
37. Z. Zhang, L. Zhao, and T. Yang, “Research on the application of artificial intelligence in image recognition technology,” presented at Journal of Physics: Conference Series, London, UK, 032118 August 2021.
38. G. Lou, and H. Shi, “Face image recognition based on convolutional neural network,” China Commun. 17, 117-124 (2020).
39. W. Rong, Z. Li, W. Zhang, and L. Sun, “An improved CANNY edge detection algorithm,” presented at 2014 IEEE international conference on mechatronics and automation, Tianjin, China, 577-582 August 2014.
40. M. Ali, and D. Clausi, “Using the Canny edge detector for feature extraction and enhancement of remote sensing images,” presented at IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, Australia, 2298-2300 July 2001.
41. E. S. Li, S. L. Zhu, B. S. Zhu, Z. Yong, C. G. Xia, and L. H. Song, “An adaptive edge-detection method based on the canny operator,” presented at 2009 International Conference on Environmental Science and Information Application Technology, Wuhan, China 465-469 July 2009.
42. I. Grishin, K. Thomson, F. Migliorini, and J. J. Sloan, “Application of the Hough transform for the automatic determination of soot aggregate morphology,” Appl. Opt. 51, 610-620 (2012).
43. N. Aggarwal, and W. C. Karl, “Line detection in images through regularized Hough transform,” IEEE Trans. Image Process. 15, 582-591 (2006).
44. D. Duan, M. Xie, Q. Mo, Z. Han, and Y. Wan, “An improved Hough transform for line detection,” presented at 2010 International Conference on Computer Application and System Modeling, Taiyuan, China, 354-357 October 2010.
45. X. H. Lee, C. C. Lin, Y. Y. Chang, H. X. Chen, and C. C. Sun, “Power management of direct-view LED backlight for liquid crystal display,” Opt. Laser Technol. 46, 142-144 (2013).
46. C. C. Sun, W. T. Chien, I. Moreno, C. T. Hsieh, M. C. Lin, S. L. Hsiao, and X. H. Lee, “Calculating model of light transmission efficiency of diffusers attached to a lighting cavity,” Opt. Express 18, 6137-6148 (2010).
47. Laptopmedia, “Top Laptop CPU Ranking,” https://laptopmedia.com/top-laptop-cpu-ranking.
48. Laptopmedia, “Top Laptop Graphics Ranking,” https://laptopmedia.com/top-laptop-graphics-ranking.

指導教授

孫慶成(Ching-Cherng Sun)

審核日期

2024-8-13

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