、 CHUN-YEON LIN合授

This class is designed for graduate or junior/senior engineering students. Students will learn image processing, model-based vision, camera model, calibration, pose estimation, stereo vision, and neural network (and AI) for robot vision.

Design of algorithms for robotic vision systems for automation, manufacturing, and service industries, image processing, optics, illumination, and feature representation.

Three hours

R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 4th Edition, 2018.

1. R. Gonzalez, R. Woods, and S. Eddins, Digital Image Processing using Matlab, 2nd ed., Prentice Hall, 2009. 2. D. H. Ballard and C. M. Brown, Computer Vision, Prentice Hall. 1982. 3. B. K. P. Horn, Robot Vision, MIT Press. 1986. 4. N. Zuech, Applying Machine Vision, Wiley Interscience. 1988. 5. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, V1 & 2, Addison Wesley. 1992. 6. F. van der Heijden, Image Based Measurement Systems, John Wiley and Sons, 1995. 7. E. R. Davies, Computer and Machine Vision: Theory, Algorithm, & Practicalities, 4th ed., Acad. Press, 2012. 8. Linda G. Shapiro and George C. Stockman, Machine Vision, Prentice Hall, 2001. 9. D. A. Forsyth, and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall. 2nd ed., 2011. 10. R. Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag, London, 2011.