機器人視覺是近年來發展相當蓬勃的ㄧ項研究，在機器人視覺研究上又可細分成相當多的項目，例如物體辨識、人臉識別、標誌判讀等，各種不同的研究項目，在這類的研究又可分成動態視覺及靜態視覺，在此我們所設定研究題目是屬於動態視覺的部份，以立體視覺為主要研究方向，立體視覺是利用兩具以上的攝影機所取得的影像來計算，計算目標物的立體位置，而我們所使用的攝影機是PTZ(Pan, Tilt, Zoom)攝影機，利用它我們便可以模擬出人眼睛的轉動方式，並對我們所設定的目標物加以追蹤，再這裡我們模擬了各種人類眼睛可能擁有的轉動方式，甚至人類眼睛的死角位置，我們都可以去取得目標物的立體座標資訊，在取得目標物座標資訊後，我們可以利用這個立體座標資訊，利用雷射筆來做更細部的追蹤，在這裡為了提升影像方面的運算處理速度，我們利用DSP模組，作為我們運算的平台，並利用它來對我們所使用的各項裝置做控制。

Machine Vision has become a popular research topic in recent years, and also widely be used in many domains, for example: object recognition, human face recognition and sign recognition, etc. In general, machine vision can be divided in static and dynamical vision. In the dynamical vision research are also subdivided into many detail parts. In this study, we focus on dynamical vision for real-time operating approach. We use binocular stereo vision to be our main architecture. We obtain the images by two or more cameras, and then we use these images to find out the target and calculate its position. We choose the PTZ (Pan Tilt Zoom) camera to be our experimental camera type, and use it to simulate the human eyes moving, and track dynamical moving target. We also simulate several difference human eyes moving behaviors, for example: cockeyes, single lens tracking, then get the target position. Even the target stay in the dead space. We also can get the information from the target, and use laser to track the object, after getting the target coordinate information. In order to achieve real-time computing requirement, all of our implementation are based on the DSP (Digital Signal Processor) module.

[1] N. Wanopoulos, N. Vasanthavada and R. L. Baker, “Design of an Image Edge Detection Filter Using the Sobel Operator,” IEEE Journal of Solid-State Circuits, Vol. 23, No. 2, pp. 358-367, Apr. 1988.
[2] T. L. Hwang and J. J. Clark, “On Local Detection of Moving Edges,” Proceedings of IEEE International Conference on Pattern Recognition, Vol. 1, pp. 180-184, 1990.
[3] S. Yalamanchili, W. N. Martin and J. K. Aggarwal, “Extraction of Moving Object Description via Differencing”, Computer Graphics and Image Processing, Vol. 18, pp. 188-201, 1982.
[4] C. R. Wren, A. Azarbayejani, T. Darrell and A. Pentland. “Pfinder: Real-Time Tracking of the Human Body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, pp. 780-785, 1997.
[5] I. Haritaoglu, D. Harwood and L. S. Davis, “Hydra: Multiple People Detection and Tracking Using Silhouettes,” Proceedings of the Second IEEE Workshop on Visual Surveillance, pp. 6-13, June 1999.
[6] B. K. P. Horn and B. G. Schunck, “Determining Optical Flow,” Artificial Intelligence, Vol. 17, No. 1-3, pp. 185-203, 1981.
[7] R. Brunelli and T. Poggio,” Template Matching: Matched Spatial Filters and Beyond,” Pattern Recognition, Vol. 30, No. 5, pp. 751- 768, 1997
[8] M. S. Lew, N. Sube, and T. S. Huang, “Improving Visual Matching,” IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 58-65, 2000.
[9] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd Edition, Prentice Hall, pp. 523-528, 2002
[10] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd Edition, Prentice Hall, pp. 91-108, 2002.
[11] P. Y. Chen, “A Robust Visual Servo System for Tracking an Arbitrary- Shaped Object by a New Active Contour Method,” Proceeding of the 2004 American Control Conference, Vol. 2,pp. 1516-1521, July 2004.
[12] D. Chai and N. Ngan, “Face Segmentation Using Skin-Color Map in Videophone Application,” IEEE Trans. Circuits and Systems for Video Techniques, Vol. 9, No. 4, pp. 551-564, June 1999.
[13] A. Rosenfield and M. Thurston, “Edge and Curve Detection for Visual Scene Analysis,” IEEE Transactions Computation, Vol. C-20, pp. 562-569, 1971.
[14] R. A. Bioe and I. J. Cox, “An Analysis of Camera Noise,” IEEE Transactions Pattern Analysis Machine Intelligence, Vol. 14, No. 6, pp. 671-674, 1992.
[15] D. Hearn and M. P. Baker, Computer Graphics, 2nd Edition, Prentice Hall, New York, pp. 49-81, 1994.
[16] P. J. Burt and E. H. Adelson, “The Laplacian Pyramid as a Compact Image Code,” IEEE Transactions Communications, Vol. COM-31, No. 4, pp. 532-540, 1983.
[17] S. R. Gunn, “On the Discrete Representation of the Laplacian of a Guassian,” Pattern Recognition, Vol. 32, No. 8, pp. 1463-2472, 1999.
[18] R. J. Qian and T. S. Huang, “Optical Edge Detection in Two-Dimensional Image,” IEEE Transactions Image Processing, Vol. 5, No. 7, pp. 1215-1220, 1996.
[19] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice-Hall, New Jersey, pp. 116-123, 2002.
[20] J. Canny, “Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, No. 6, pp. 679-698, 1986.