Examination arrangement

Course content

Digital image fundamentals:

• Image sensing and acquisition: analogue to digital conversion, image sampling and quantization, look-up table conversions, scaling, mathematical tools, digital image formats representation and description.

Intensity Transformations and spatial filtering:

• image negatives, log, gamma transformations, thresholding, histogram processing, histogram equalization, spatial correlation and convolution, smoothing and sharpening filters, gradient.

Filtering in the frequency domain:

• Complex numbers, Fourier series, Fourier transform, impulses and their sifting property, sampling, aliasing, function reconstruction from sampled data, 2D-impulse and its sampling property, smoothing and sharpening filters, selective filtering.

Image Restoration and reconstruction:

• Noise and properties, noise probability density functions, restoration from noise in spatial domain, noise reduction in frequency domain, inverse filtering, geometric mean filter.

Image Segmentation:

• Point, line, edge detection, edge linking and boundary detection, thresholding, region based segmentation, split and merge algorithms, region growing segmentation using watersheds.

Object Recognition:

• Patterns and pattern classes, recognition based on decision-theoretic methods, structural methods, high level descriptors.

Learning outcome

On completion of this course the students:

Knowledge -Possess an understanding of the fundamental characteristics of digital systems used in imaging, together with general concepts of science, quantitative methods. -Possess advanced knowledge of (i.e. to describe, analyse and reason about) basic algorithms for image manipulation, characterization, filtering, segmentation, feature extraction and template matching in direct space and Fourier space. -Possess advanced knowledge of how monochrome digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation. -Possess advanced knowledge of methods of capturing and reproducing images in digital systems. -Possess knowledge and understanding of the mathematical methods commonly used for representing, compressing and processing signals and images.

Skills -Are able to use mathematical techniques in colour imaging and demonstrate the use of tools such as spreadsheets and specialist maths applications to solve problems in signal and image processing. -Are able to to explore a range of practical techniques, by developing their own simple processing functions using library facilities and tools such as, e.g., Matlab or Python. -Are able to implement the techniques in the topics studied and compare their performances in certain image processing tasks. -Are able to use relevant and suitable methods when carrying out research and development activities in the area of image processing

General competence -Have the learning skills to continue acquiring new knowledge and skills in a manner that is largely self-directed -Are able to contribute to innovative thinking and innovation processes

Learning methods and activities

• lectures
• Lab work
• Assignments
• E-learning

Additional information: -The course will be offered both as an ordinary campus course and in a flexible way to off-campus students. Lecture notes, e-lectures and other types of e-learning material will be offered through Blackboard. Communication between the teachers and the students, and among the students, will be facilitated by Blackboard.

Compulsory requirements:

The portfolio will include compulsory small assignments and a bigger project and the scope and the deadlines for the assignments and the project are announced 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. 

Further on evaluation

No re-sit examination of the portfolio.

Forms of assessment:

The portfolio consists of up to 4 smaller assignments and one bigger project and is handed in individually. There is continuous assessment of each of the assignments before the final submission date of the portfolio. The portfolio will provide a pass or not pass of the course.

Specific conditions

Recommended previous knowledge

The following topics must be mastered from the bachelor level: -Fundamental programming -Fundamental calculus including trigonometric functions, logarithms and the exponential function -Fundamental linear algebra -Fundamental complex calculus including the complex exponential function -Arithmetic and geometric series

Course materials

Course books: -Discrete Fourier Analysis and Wavelets - Applications to Signal and Image Processing, by Broughton, S. Allen and Kurt Bryan (2008). New Jersey: John Wiley & Sons, Inc. -Digital Image Processing, 3rd Edition (DIP/3e), by Rafael C. Gonzalez and Richard E. Woods, Prentice Hall (2008)

Further reading material: -Digital Image Processing Using MATLAB (DIPUM), by Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Prentice Hall (2004). -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