Course Catalog 2007-2008

SGN-3056 DIGITAL IMAGE PROCESSING II, 5 cr
Digital Image Processing II

Courses persons responsible
Karen Eguiazarian

Lecturers
Karen Eguiazarian

Language of Instruction
classroom exercises will be given in English only

Lecturetimes and places
Per IV: Wednesday 14 - 16, TB223
Per IV: Thursday 14 - 16, TB224

Implementations
  Period 1 Period 2 Period 3 Period 4 Period 5 Summer
Lecture - - - 4 h/week - -
Exercise - - - 2 h/week - -
Assignment - - - - 50 h/per -
(Timetable for academic year 2007-2008)

Objectives
Students get in-depth view of selected topics of image processing and perform practical tasks in image processing laboratory.

Content
Content Core content Complementary knowledge Specialist knowledge
1. Image representation       
2. Image data compression.       
3. Object recognition       
4. Image filtering, segmentation and restoration.       
5. Practical image processing tasks.       

Requirements for completing the course
50% attendance at lectures is required. At least 30% of classroom-exercise tasks and 3 assignments.

Evaluation criteria for the course

  • Course grade is computed based on four units as follows: Classroom exercises, 40% of the mark. First laboratory work, 10% of the mark. Second laboratory work, 20% of the mark. Third laboratory work, 30% of the mark All four units must be passed otherwise final mark is not given. There is no exam.

  • Used assessment scale is numeric (1-5)

    • Study material
    Type Name Auhor ISBN URL Edition, availability... Exam material Language
    Book "Digital Image Processing" R. Gonzalez and R. Woods     2nd ed., Prentice-Hall, 2002 No  English 
    Book "The Image Processing Handbook" John Russ     Fourth Edition, CRC Press, 2002 No  English 
    Book "Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards" Y. Shi, H. Sun     CRC Press, 1999 No  English 
    Book "The Transform and Data Compression Handbook" K. Rao and P. Yip     CRC Press, 2001 No  English 
    Lecture slides "Digital Image Processing II" Karen Egiazarian et al.       No  English 
    Book Local Approximation Techniques in Signal and Image Processing V.Katkovnik, K.Egiazarian, and J.Astola 978-0819460929 project page www.cs.tut.fi/~lasip   No   

    Prerequisites
    Code Course Credits M/R
    OHJ-1100 OHJ-1100 Programming I 4 Recommendable
    OHJ-1106 OHJ-1106 Programming I 4 Recommendable
    SGN-3010 SGN-3010 Digital Image Processing I 5 Mandatory
    SGN-3016 SGN-3016 Digital Image Processing I 5 Mandatory

    Prequisite relations (Sign up to TUT Intranet required)

    Additional information about prerequisites
    Either SGN-3010 or SGN-3016 is required.

    Remarks

    Laboratory works are done after theory and classroom exercise sessions during the following teaching period.

  • Partial passing of course must be in connection with the same round of implementation.

  • The course is suitable for postgraduate studies.

    • Distance learning

  • ITC utilized during the course

    • - In distributing and/or returning exercise work, material etc

  • Estimate as a percentage of the implementation of the course

    • - Contact teaching: 43 %
    - Distance learning: 0 %
    - Proportion of a student's independent study: 57 %

  • Description of the course implementation from ICT point of view

    • 50% of lectures are mandatory.

    Scaling
    Methods of instructionHours
    Lectures 36
    Exercises 36
    Laboratory assignments 61
    Total sum 133

    Principles and starting points related to the instruction and learning of the course

  • Theory is presented at the lectures. Exercises cover practical problems. Exercises are done at home in advance and solutions are presented and dicussed in the classroom. Laboratory works are done after theory and classroom-exercise sessions during the following teaching period.

    • Correspondence of content
    8002153 Digital Image Processing II

    Course homepage

    Last modified 07.02.2008
    Modified bySari Peltonen