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IDIG4321

Introduction to Color Image Processing

Choose study year
Credits 7.5
Level Second degree level
Course start Autumn 2024
Duration 1 semester
Language of instruction English
Location Gjøvik
Examination arrangement Aggregate score

About

About the course

Course content

This course develops an understanding of the fundamental characteristics of digital systems used in imaging and introduces basic methods and techniques for processing color images. The course covers basic algorithms for image manipulation, characterization, filtering, segmentation, feature extraction and template matching in direct space and Fourier space. The course provides the opportunity for students to explore a range of practical techniques, by developing their own simple processing functions using Matlab.

The course outline includes and not limited to:

  • Introduction and Image Processing Fundamentals
  • Image acquisition (Sampling and quantization)
  • Image models and representation
  • Introduction to color imaging.
  • Statistical analysis of image signals
  • Image Filtering (linear, nonlinear)
  • Image enhancement and restoration
    • Denoising, contrast enhancement,
    • Deblurring
  • Image Segmentation
    • Gray-level thresholding
    • Edge detection
    • Region based segmentation
  • Morphological Image Processing
    • Dilation and Erosion
    • Opening and Closing
  • Elements of image compression
  • Lossy image compression - basics notions
  • Spatial domain based methods
    • Block Truncation Coding
    • Vector Quantization Coding
    • Predictive Coding
    • Laplacian pyramid image coding
  • Frequency domain methods
    • DCT based methods
    • Subband coding
    • Wavelet based methods

Learning outcome

on completion of this course the student will acquire knowledge which allows her/him to:

  • Understand (ie to describe, analyze and reason about) how monochrome digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation. methods of capturing and reproducing images in digital systems.
  • Understand (ie to describe, analyze and reason about) how color digital images are represented, manipulated, encoded and processed.
  • Make appropriate use of mathematical techniques in color imaging. Demonstrate the use of tools such as spreadsheets and specialist maths applications to solve color imaging problems.

Learning methods and activities

  • lectures
  • Lab work
  • Assignments

Compulsory requirements:

To be eligible for the final exam and project hand-in students are expected to deliver and get approved at at least 80% of all the assignments during the semester

If the minimum number of students registered for the course is less than 5, the course may not run during a semester.

Compulsory assignments

  • Mandatory assignment

Further on evaluation

See "Compulsory assignment" explained in Teaching Methods.

The student must obtain a passed grade in both two mandatory elements of assessment (the written exam and the projects) in order to complete the course.

The final project, its scope and the deadlines for the assignments and the project are announced during the semester. Students are expected to perform independent coding. That is, programming and writing documentation by their own without getting aid from AI tools. Teaching assistants will be able to help students during the tutorial/lab session.

There will be a re-sit for the written exam at the end of February or in March. The re-sit examination can be oral.

The projects need to be resubmitted next time the course is run.

Specific conditions

Admission to a programme of study is required:
Applied Computer Science (MACS)

Course materials

Course book:

  • Digital Image Processing, 4th Edition (DIP / 4th), by Rafael C. Gonzalez and Richard E. Woods, Prentice Hall (2017)
  • Digital Image Processing Using MATLAB (DIPUM), by Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Pearson (2018).

Further reading material:

  • Color Image Processing: Methods and Applications (Image Processing), by Rastislav Lukac & Kostantinos N. Plataniotis, CRC (2006)
  • The Image Processing Handbook, Fifth Edition (Image Processing Handbook), by John C. Russ, CRC (2006)

Credit reductions

Course code Reduction From
IMT4305 7.5 sp Autumn 2022
This course has academic overlap with the course in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Computer Science

Contact information

Course coordinator

Department with academic responsibility

Department of Computer Science