CSU921-924 Reading Course

Prof. Patrick Wang, 260WVH, (617)373-3711, pwang@ccs.neu.edu


The main goal is to explore advanced topics of Artificial Intelligence, Learning Methodologies and Applications to Pattern Recognition/Computer Vision/Web Design Topics in one of the following selected fields or closely related areas :

  • 3D Object Recognition and 2D Image Pattern Analysis With Learning

  • Thinning Methodologies (Parallel and Sequential) [pdf] and [ps]
  • Words (Characters) Learning/Understanding/Recognition Using Intelligent Techniques

  • Chinese OCR (Optical Character Recognition)

  • 3D Imaging in Interactive Learning Virtual Environment using Internet and IM/Webcam

    • Reading references for this course will be mainly selected from including (but not exclusive) following publications and/or related papers/books :

  • ARTIFICIAL INTELLIGENCE: A Modern Approach , 2nd Edition, by Stuart Russel and Peter Norvig, Prentice Hall publisher, (2003), Chapters 18, 19, 21 in VI and 24 in VII
  • Artificial Intelligence, by Patrick Winston, Addison-Wesley (1994), Chapters 26, 27
  • Handbook of Pattern Recognition & Computer Vision, ed by C.Chen, L.Pau, P.Wang, WSP (1999), Chapters in Parts III & V
  • Handwritten Bank Check Recognition of Courtesy Amounts, International Journal of Imaging Graphics (IJIG), 2004 (with A.Gupta and R. Palacios) to appear [doc file]
  • Feedback based architecture for reading courtesy amounts on checks Int. J. Electronic Imaging (IJEI), (with A.Gupta, R.Palacios), 12(1) 194-202, 2003 [pdf file], [ps file]
  • 3D Vision and Object Recognition via Internet Web (with Yi, Min) [ppt], and [pdf] Int. J. Pattern Recognition and Artificial Intelligence (IJPRAI), 2001
  • High Level Visualization, Representation, Understanding, and Recognition of 3D Articulated Objects, by P. Wang The Encyclopedia of Microcomputers, ed. A. Kent, J. Williams, Marcer Dekker Pub. Co., 1999 [pdf file]
  • Analysis, Learning, and Recognition of Articulated Line Drawing Images, by P.Wang Int. J. CSIM, Special Issue on Image Recognition, 1998 v1-2 [pdf file]

    • There will be no formal class schedules. Students will be expected to meet with the instructor for about one hour each week, to discuss selected papers, at mutually agreed upon time. If the student has any difficulties, the meeting time may be changed with advance notice. The final grade will be based on a term report, which will be due in the same week of the final exam. Note that the amount of work needed to be done for CSU922 (2 credit hours) is about half of CSU924 (4 credit hours) and so on.

    Week Contents (being converted to semester system, with more weeks)
    I Introduction to the Course and Assign papers/articles
    II Student(s) read articles and discuss with instructor
    III Student(s) continue reading articles and discuss with instructor
    IV Concentrate on a couple of articles to pursue
    V Search for related articles/papers of the concentration
    VI Continue search for related articles/papers of the concentration
    VII Draft a preliminary report summary
    VIII Discuss the focus, advantages, and what have learned so far
    IX Continue discussing the focus, advantages, and what have learned so far
    X Discuss assigned articles together with other related materials
    XI Comments on existing methodologies, their advantages vs disadvantages
    XII Discuss what might be improved, or any new ideas
    XIII Discuss final conclusions, and draft term report
    XIV Presentation of final conclusions, and draft term report
    XV (Final Exam Week) Term Report Due


    The final report will basically follow the format as follows:

  • [Title of topics]
  • [Abstract]
  • [Main Body of the Content] (Including theretical principles, Brief historical background, and Applications)
  • [Experimental Results]
  • [Analysis, Discussions and Conclusions] (Including comparions with other similar methods in the literature)
  • [Bibilography]
  • [Appendix] (Including any software program being used in the course)


    • The total length of the term report will be no longer than 15 pages, double space, including all context, figures, tables, and references, except Appendix, which can be as long as it can be (but should be relevant to the report)
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    Dr. Patrick S.P. Wang, Professor & IAPR Fellow
    College of Computer and Information Science, Northeastern University
    360 Huntington Avenue #260 WVH, Boston, MA 02115
    Phone: (617) 373-3711(Voice) --- Fax: (617) 373-5121
    Home Page: http://www.ccs.neu.edu/home/pwang
    pwang@ccs.neu.edu,
    For any comments, please send to one of the above email addresses. Thank you.