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ELEC-Y412

Image processing

academic year
2024-2025
2025-2026

Course teacher(s)

Adrian MUNTEANU (Coordinator)

ECTS credits

5

Language(s) of instruction

english

Course content

The fundamental grounds in digital image processing are set by linear algebra, digital signal processing and statistics. Due to insufficient knowledge of the imaged scene, it remains an art on top of a scientific discipline to highlight the relevant information or to extract it from images. This extracted information varies depending on the goals that are pursued or the application field (context) that is considered. This course is focused on processing of measured and discretized image data, without taking into account a priori contextual models of the scene.

Detailed content:

1) Global Image Transforms, Discrete Karhunen Loeve Transform - KLT; Proofs and Construction of KLT basis images; Application of KLT for Image Compression; Discrete Fourier Transform; Discrete Cosine Transform – DCT, application of DCT in image compression.

2) Wavelet Transform; Time-Frequency Representations, uncertainty principle; Continuous Short-Time Fourier Transform; Continuous Wavelet Transform; Frames; The Multiresolution Representation; Integer Wavelet transform and the lifting scheme; Application Examples

3) Image enhancement and image restoration: histogram operators, noise reduction with linear and non-linear filters, unsharp masking, pseudo-colouring, clipping, histogram stretching, image restoration.

4) Image segmentation: Thresholding, Edge detection based on the gradient magnitude, Edge detection based on the zero crossings, Canny edge detection, Deformable contours and surfaces, Region based techniques (split-and-merge, watersheds, multi-resolution segmentation); Pixel/segment classification, unsupervised clustering (e.g. k-means).

5) Mathematical Morphology: general theory for binary and gray value images, examples of operators, reconstruction filters, top-hat and bottom-hat filters.

Objectives (and/or specific learning outcomes)

  • The course introduces image representation principles and digital image processing algorithms, including image transforms, image enhancement and restoration, edge detection, image segmentation and image compression. The course describes generic techniques that find their application in a variety of fields, such as visual inspection, medical imaging, compression and transmission of images and video, multimedia applications, machine vision and remote sensing.

  • With this course, the student acquires the necessary skills and gathers an in-depth theoretical and practical knowledge up to a stage that he/she should be able to solve various image processing problems.

Prerequisites and Corequisites

Courses requiring this course

Teaching methods and learning activities

Courses, exercises, self-study

References, bibliography, and recommended reading

  • Digital Picture Processing (2nd Ed.), A. Rosenfeld and A. Kak, Vol. 1 and 2, 1982

  • Fundamentals of Digital Image Processing, A. Jain, Prentice Hall, 1989

  • Digital Image Processing (3rd Ed.), R. Gonzalez, Addison and Wesley, 1992

  • The World according to Wavelets, Barabara Burke Hubbard,A.K. Peters, Wellesley, Massachussets, 1998, ISBN 1-56881-072-5 5

  • High performance compression of visual information - a tutorial, Olivier Egger, Pascal Fleury, Touradj Ebrahimi, Murat Kunt, Review Part I: Still Pictures", Proc. IEEE, Vol. 87, No.6, June 1999

  • Wavelets and subband coding, Martin Vetterli, Jelena Kovacevic, Prentice Hall, ISBN: 0130970808, 1995

  • A wavelet tour of signal processing, S.Mallat, Academic Press, ISBN: 012466606X, 1998.

Contribution to the teaching profile

This teaching unit contributes to the following competences:

  • In-depth knowledge and understanding of the advanced methods and theories to schematize and model complex problems or processes

  • Reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)

  • Correctly report on research or design results in the form of a technical report or in the form of a scientific paper

  • Collaborate in a (multidisciplinary) team

  • Work in an industrial environment with attention to safety, quality assurance, communication and reporting

  • Think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information

  • A creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society

  • A critical attitude towards one’s own results and those of others

  • The flexibility and adaptability to work in an international and/or intercultural context

  • An attitude of life-long learning as needed for the future development of his/her career

  • Has an active knowledge of the theory and applications of electronics, information and communication technology, from component up to system level.

  • Has a broad overview of the role of electronics, informatics and telecommunications in industry, business and society.

  • Is able to analyse, specify, design, implement, test and evaluate individual electronic devices, components and algorithms, for signal-processing, communication and complex systems.

Other information

Contacts

prof. Adrian MUNTEANU

Electronics and Informatics department

Pleinlaan 2, B-1050 Brussels

acmuntea at etro.vub.ac.be

Evaluation

Method(s) of evaluation

  • Other

Other

Oral and Written examination

Exam procedure: (1) 90 minutes for a detailed preparation and structuring of the answers (without course syllabus), and (2) approximately 20 minutes of discussion with the teacher about the main questions and a number of secondary questions in other domains than the main questions.

Project presentation: the students will be organized in groups and each group will receive a specific project assignment concerning an image processing problem that will have to be solved and practically implemented in software. A report detailing the design and implementation will have to be provided as well. The contribution brought by each student in the group will have to be indicated during the defense of the project.

Mark calculation method (including weighting of intermediary marks)

The final score is given by 80% of the score obtained during the exam and 20% on the score obtained for the project.

Programmes