NDAA09027U Signal and Image Processing
Signal and Image Processing introduce the basic computer tools to deal with sampled data, especially the images, i.e., the 2D spatially regular ones but 1D application examples will are presented.
Applications of Signal and image processing range from time series analysis include sound or financial data to complex imaging devices, from astronomy to surveillance to medicine and biology. This make this course an ideal supplement for students coming from various fields of science.
Students will not only acquire a theoretical knowledge of signal processing but also good implementation practices. There will be weekly exercises, both theoretical, so as to help students understand the different concepts, as well as practical ones, so as to apply them, including filtering, denoising, segmentation.Topics to be covered are:
- Signal and image processing fundamentals
- Sampling, Sampling theorem, Fourier transform
- Convolution, linear and nonlinear filtering
- Image restoration, inverse filtering
- Image histograms, colour image processing
- Image segmentation
- Multiresolution processing
- Image compression
- Representation, and description.
Evaluate which signal / image processing methods and pipeline of methods is best suited for solving a given signal problem. Understand the implications of theoretical theorems and being able to analyse real problems on that basis.
Apply basic signal processing methods to solve basic signal processing problems.
Convolution and Correlation
Filtering and denoising
Elementary Segmentation methods
Elementary representations and description of image content
- Practical exercises
- Project work
- Theory exercises
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- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous evaluation of written assignments evaluated using internal grading and the 7-point grading scale.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Oral examination (25 minutes including grading) in course curriculum without preparation. Internal grading using the 7-point grading scale.
Criteria for exam assesment
Discuss and apply the theoretical basics of digital signal and image processing
Reflect the linear processing of signals and design and apply digital filters for discrete signals.
Explain and identify different types of noise, design noise removal algorithms for image restoration and solve statistical linear inverse filtering problems for images.
Compare Fourier analysis to multiresolution analysis, relate the fundamental concepts of multiresolution analysis, and perform time/space-frequency analysis for signals and images.
Analyze the image histograms, and transform the images to another forms to enhance the visual content of the image or to facilitate easier interpretation and processing.
Explain fundamental image segmentation approaches and implement them to extract homogeneous regions on the images.
Explain the principles of image compression methods and design and implement lossless and lossy compression methods for image compression.
Relate and illustrate elementary representation methods in description of image content