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Advanced Computer and Robot Vision Course

Explore high-level computer and robot vision techniques, including perspective geometry, motion analysis, and object modeling, through extensive course projects and examinations. Dive deep into the world of computer vision with this elective course.

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Advanced Computer and Robot Vision Course

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  1. Computer and Robot Vision II Chapter 0 Presented by: 傅楸善 & 顏慕帆 0933 373 485 r94922113@ntu.edu.tw 指導教授: 傅楸善 博士

  2. Course Number: 922 U1200 • Credits: 3 • Time: Tuesday 6, 7, 8 (2:20PM~5:20PM) • Classroom: New CSIE Classroom 107 • Classification: Elective for junior, senior, and graduate students • Prerequisite: Computer Vision (I), or Digital Image Processing • Instructor: Chiou-Shann Fuh • Office: New Computer Science and Information Engineering 327 • Phone: 23625336 ext.327, 23630231 ext. 3232 ext. 327 • Office Hours: Tuesday 11AM~12 noon • Objective: To learn computer and robot vision through extensive course projects DC & CV Lab. NTU CSIE

  3. Textbook: R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, Vol II Addison Wesley, Reading, MA, 1993 • Reference: B. K. P. Horn Robot Vision, MIT Press, Cambridge, MA, 1986 • Reference: R. Jain, R. Kasturi and B. G. Schunck Machine Vision, McGraw-Hill, New York, 1995 • Projects: assigned weekly on first few weeks(20%) and a term project (30%)􀀀 • Examinations: one midterm (20%) and one final (30%) DC & CV Lab. NTU CSIE

  4. Content: • This is the second semester of a fast pace course which covers robot and computer vision. • The first semester covers low-level vision and mostly no reference to three dimensions • This semester covers higher-level techniques: DC & CV Lab. NTU CSIE

  5. 12. Illumination 13. Perspective Projective Geometry 14. Analytic Photogrammetry 15. Motion and Surface Structure from Time Varying Image Sequences 16. Image Matching 17. The Consistent-Labeling Problem 18. Object Models and Matching 19. Knowledge-Based Vision 20. Accuracy 21. Glossary of Computer Vision Terms DC & CV Lab. NTU CSIE

  6. Bibliography • D. H. Ballard and C. M. Brown, Computer Vision, Prentice-Hall, Englewood Cliffs, NJ, 1982. • G. A. Baxes, Digital Image Processing, Wiley, New York,1984. • K. Castleman , Digital Image Processing, Prentice-Hall, Englewood, Cliffs, NJ, 1996. • E. R. Davies, Machine Vision: Theory, Algorithms, Practicalities,2ndEd., Academic Press, San Diego,CA, 1997 • R.C.Gonzalez and R.E. Woods, Digital Image Processing, Addison Wesley, Reading, MA,1992 • EGose,RJohnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis, Prentice-Hall, Englewood Cliffs, NJ, 1996. • A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1990. • J.S.Lim, Two-Dimensional Signal and Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1990. • D. Marr, Vision, W.H.Freeman, San Francisco,1982. • V. S. Nalwa, A Guided Tour of Computer Vision, Addison Wesley, Reading, MA, 1993. • W. K. Pratt, Digital Image Processing, 2nd ed., Wiley-Interscience, New York, 1991. • R. J. Schalkoff, Digital Image Processing and Computer Vision: An Interduction to Theory and Implementations, Wiley, New York, 1989. • R. J. Schalkoff, Pattern Recognition: Statistical, Structural, and Neural Approaches, Wiley, New York, 1992. DC & CV Lab. NTU CSIE

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