5 ECTS credits
125 h study time
Offer 1 with catalog number 4010977ENR for all students in the 2nd semester at a (E) Master - advanced level.
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: General Model; Physical Meaning of the expansion in basis images; How to calculate the coefficients?; Separable Transforms; Direct, Forward Transforms, Orthonormal Case; Discrete Karhunen Loeve Transform - KLT; Proofs and Construction of KLT basis images; Application of KLT for Image Compression; Discrete Fourier Transform (1D, 2D definitions, existence, terminology, properties, display of Fourier coefficient images); Discrete Cosine Transform – DCT (1D, 2D definitions), application of DCT in image compression.
2) Wavelet Transform: Drawbacks of the Fourier Analysis; 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 (Embedded Zerotree coding of the Wavelet Coefficients, Wavelet-based Quadtree coding and Multi-scale Edge detection via CWT); Modulation Domain Analysis – self-study.
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 (Sobel and Prewitt filters), Edge detection based on the zero crossings of the Laplacian of Gaussian, Canny edge detection, Deformable contours and surfaces, Region based techniques (split-and-merge, watersheds, multi-resolution segmentation - the concept of scale space); Pixel/segment classification (supervised classification by means of linear and quadratic discriminant analysis), unsupervised clustering (e.g. k-means).
5) Mathematical Morphology: general theory for binary and gray value images, examples of operators (erosion, dilation, opening and closing), reconstruction filters, top-hat and bottom-hat filters.
Course notes of the individual sections can be obtained from: ftp://ftp.etro.vub.ac.be/
Complementary study material:
- 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.
- 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.
This course contributes to the following programme outcomes of the Master in Electronics and Information Technology Engineering:
The Master in Engineering Sciences has in-depth knowledge and understanding of
3. the advanced methods and theories to schematize and model complex problems or processes
The Master in Engineering Sciences can
4. reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
6. correctly report on research or design results in the form of a technical report or in the form of a scientific paper
8. collaborate in a (multidisciplinary) team
9. work in an industrial environment with attention to safety, quality assurance, communication and reporting
11. think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information
The Master in Engineering Sciences has
12. a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
13. a critical attitude towards one’s own results and those of others
15. the flexibility and adaptability to work in an international and/or intercultural context
16. an attitude of life-long learning as needed for the future development of his/her career
The Master in Electronics and Information Technology Engineering:
17. Has an active knowledge of the theory and applications of electronics, information and communication technology, from component up to system level.
19. Has a broad overview of the role of electronics, informatics and telecommunications in industry, business and society.
20. Is able to analyze, specify, design, implement, test and evaluate individual electronic devices, components and algorithms, for signal-processing, communication and complex systems.
The final grade is composed based on the following categories:
Oral Exam determines 30% of the final mark.
Written Exam determines 30% of the final mark.
PRAC Practical Assignment determines 40% of the final mark.
Within the Oral Exam category, the following assignments need to be completed:
Within the Written Exam category, the following assignments need to be completed:
Within the PRAC Practical Assignment category, the following assignments need to be completed:
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.
The final score is given by 60% of the score obtained during the exam and 40% on the score obtained for the project.
This offer is part of the following study plans:
Master of Electronics and Information Technology Engineering: Standaard traject (only offered in Dutch)
Master of Photonics Engineering: Standaard traject (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (only offered in Dutch)
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics
Master of Electrical Engineering: Standaard traject BRUFACE J
Master of Teaching in Science and Technology: computerwetenschappen (120 ECTS, Etterbeek) (only offered in Dutch)