為了實現車輛週遭無視線死角的概念，我們提出一套車體環場鳥瞰監視系統。分別在車輛前後及兩側架設廣角相機，相機所拍攝影像經由轉換及組合成一個俯視車輛週遭環境的鳥瞰影像提供給駕駛者，藉以達到安全停車輔助的目的。
廣角相機提供較大的視野範圍，但也帶來嚴重的畫面扭曲問題。為了找出相機扭曲係數，我們利用投影成像原理建立扭曲係數的優劣評估準則。利用精確的扭曲係數將扭曲的原始影像還原成正確的透視投影影像。
整個系統的實做觀念是，我們假設在車輛上方有一個俯視車輛的虛擬相機，再將各扭曲校正後的影像轉換成虛擬相機所拍攝的影像。其過程可分為兩個步驟；首先原始影像點反投影至地平面，接著再從地平面投影至虛擬相機影像平面上。因為相機相對於車輛的位置固定，所以只需一次相機校正程序即可完成上述連續影像序列的轉換。
扭曲校正及鳥瞰轉換皆需利用相機內外部參數，我們使用一個基於平面校正板的相機校正技術。此相機校正技術只需一個參考平面，且允許任意變換相機的空間位置及指向，校正過程簡易且有彈性，取得的參數亦很精確。
最後我們利用一個簡單的統計分析技巧，將一影像的色彩特徵轉移至另一影像，讓四部相機所拍攝的影像能夠有比較一致的色彩。
我們用四部簡易型的相機架設在一部中古車上實現我們所提的“環場鳥瞰監視停車輔助系統”。由於設備不夠精良，在四部相機影像的對位上還有一些誤差。未來更新設備，可以讓我們的系統表現更完美。

To realize the concept of “No blind spot around the vehicle”, cameras are used to support the driver''s visibility. We propose a system that employs four wide-angle cameras mounted in the front, rear, and both sides of a vehicle to capture images; images are then transformed and combined a bird-view image of the surrounding area of the vehicle to provide to the driver.
To generate a bird-view of the surrounding area, we define a virtual camera above the vehicle. The bird-view image is constructed in two steps. First, image pixels of the wide-angle cameras are back-projected to the ground plane. Second, the ground points are projected to the virtual camera.
The system is composed of four cameras with wide-angle lenses to get a wide field of view, but introducing a heavy distortion on images. To find the distortion parameter of a camera, we assume the basic property of the pinhole camera model: line segments in the 3-D space will always project as 2-D lines in the image plane. Concerning the problems of distortion removal and inverse perspective mapping with the knowledge of the intrinsic and extrinsic parameters of cameras have to be solved. A technique for camera calibration using a planar pattern is presented.
Finally, we use a simple statistical analysis method to impose one image’s color on another, which improves the quality of the results. In this thesis, we show how to transform and combine images from four wide-angle cameras to provide a bird-view of the surrounding area of a vehicle on a single display.

[1] Bertozzi, M., A. Broggi, and A. Fascioli, “Vision-based intelligent vehicles: State of the art and perspectives,” Robotics and Autonomous Systems, vol.32, no.1, pp.1-16, 2000.
[2] Bertozzi, M., A. Broggi, M. Cellario, A. Fascioli, P. Lombardi, and M. Porta, “Artificial vision in road vehicles,” Proceedings of The IEEE, vol.90, no.7, pp.1258-1271, Jul. 2002.
[3] Bertozzi, M., A. Broggi, P. Medici, P. P. Porta, and A. Sjogren, “Stereo vision-based start-inhibit for heavy goods vehicles,” in Proc. of IEEE Intelligent Vehicles Symposium, Tokyo, Japan, Jun.13-15, 2006, pp.350-355.
[4] Brown, D. C., “Close-range camera calibration,” Photogrammetric Engineering, vol.37, no.8, pp.855-866, 1971.
[5] Caprile, B. and V. Torre, “Using vanishing points for camera calibration,” International Journal of Computer Vision, vol.4, no.2, pp.127-140, 1990.
[6] Claus, D. and A. W. Fitzgibbon, “A rational function lens distortion model for general cameras,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, Jun.20-25, 2005, vol.1, pp.213-219.
[7] Devernay, F. and O. Faugeras, “Straight lines have to be straight,” Machine Vision and Application, vol.13, no.1, pp.14-24, 2001.
[8] Ehlgen, T. and T. Pajdla, “Monitoring surrounding areas of truck-trailer combinations,” in Proc. of 5th Int. Conf. on Computer Vision Systems, Bielefeld, Germany, Mar.21-24, 2007, CD-ROM.
[9] Ehlgen, T. and T. Pajdla, “Maneuvering aid for large vehicle using omnidirectional cameras,” in Proc. of 8th IEEE Workshop on Applications of Computer Vision, Austin, Texas, 2007, pp.17.
[10] Ehlgen, T., M. Thorn, and M. Glaser, “Omnidirectional cameras as backing-up aid,” in Proc. of IEEE Int. Conf. on Computer Vision., Rio de Janeiro, Brazil, Oct.14-21, 2007, pp.1-5.
[11] Faig, W., “Calibration of close-range photogrammetry systems: Mathematical formulation,” Photogrammetric Engineering and Remote Sensing, vol.41, no.12, pp.1479-1486, 1975.
[12] Faugeras, O. and G. Toscani, “The calibration problem for stereo,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Miami Beach, FL, Jun. 1986, pp.15-20.
[13] Faugeras, O., T. Luong, and S. Maybank, “Camera self-calibration: Theory and experiments,” in Proc. of 2nd European Conf. on Computer Vision, Santa Margherita Ligure, Italy, May. 1992, vol.588, pp.321-334.
[14] Fitzgibbon, A. W., “Simultaneous linear estimation of multiple view geometry and lens distortion,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, Dec. 2001, vol.1, pp.125-132.
[15] Fleck, M. M., Perspective Projection: The Wrong Imaging Model, Technical Report TR 95-01, Computer Science, University of Iowa, 1995.
[16] Ganapathy, S., “Decomposition of transformation matrices for robot vision,” Pattern Recognition Letters, vol.2, pp.401-412, 1984.
[17] Gandhi, T. and M. M. Trivedi, “Dynamic panoramic surround map: motivation and omni video based approach,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Washington DC, Jun.20-26, 2005, pp.61.
[18] Gandhi, T. and M. M. Trivedi, “Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera,” Machine Vision and Applications, vol.16, no.2, pp.85-95, 2005.
[19] Gennery, D., “Stereo-camera calibration,” in Proc. of 10th Image Understanding Workshop, Los Angeles, CA, Nov. 1979, pp.101-108.
[20] Geyer, C. and K. Daniilidis, “Catadioptric projective geometry,” International Journal of Computer Vision, vol.45, no.3, pp.223-243, 2001.
[21] Liu, Y. C., K. Y. Lin, and Y. S. Chen, “Bird’s-eye view vision system for vehicle surrounding monitoring,” in Proc. Conf. Robot Vision, Berlin, Germany, Feb. 20, 2008, pp.207-218.
[22] Maybank, S. J. and O. D. Faugeras, “A theory of self-calibration of a moving camera,” International Journal of Computer Vision, vol.8, no.2, pp.123-152, 1992.
[23] Reinhard, E., M. Adhikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Computer Graphics and Applications, vol.21, no.5, pp.34-41, 2001.
[24] Ruderman, D. L., T. W. Cronin, and C. C. Chiao, “Statistics of cone responses to natural images: implications for visual coding,” Journal of the Optical Society of America A, vol.15, no.8, pp.2036-2045, 1998.
[25] Slama, C. C., editor., Manual of Photogrammetry, 4th edition, American Society of Photogrammetry and Remote Sensing, Falls Church, Virginia, 1980.
[26] Tsai, R. Y., “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf tv cameras and lenses,” IEEE Journal of Robotics and Automation, vol.3, no.4, pp.323-344, 1987.
[27] Wei, G. and S. Ma, “A complete two-plane camera calibration method and experimental comparisons,” in Proc. of 4th Int. Conf. on Computer Vision, Berlin, Germany, May 11-14, 1993, pp.439-446.
[28] Weng, J., P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.14, no.10, pp.965-980, 1992.
[29] Zhang, Z., “A flexible new technique for camera calibration,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.22, no.11, pp.1330-1334, 2000.
[30] Zhang, Z., “Camera calibration with one-dimensional objects,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, no.7, pp.892-899, 2004.