Module: Digital Image Analysis
|Digital Image Analysis||Lecture||4||Winter Semester|
Prof. Rolf-Rainer Grigat
Recommended Previous Knowledge:
System theory of one-dimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear time-invariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics
- Describe imaging processes
- Depict the physics of sensorics
- Explain linear and non-linear filtering of signals
- Establish interdisciplinary connections in the subject area and arrange them in their context
- Interpret effects of the most important classes of imaging sensors and displays using mathematical methods and physical models.
Students are able to
- Use highly sophisticated methods and procedures of the subject area
- Identify problems and develop and implement creative solutions.
Students can solve simple arithmetical problems relating to the specification and design of image processing and image analysis systems.
Students are able to assess different solution approaches in multidimensional decision-making areas.
Students can undertake a prototypical analysis of processes in Matlab.
Students can solve image analysis tasks independently using the relevant literature.
ECTS-Credit Points Module:
Workload in Hours:
Independent Study Time: 124, Study Time in Lecture: 56
Course: Digital Image Analysis (Lecture)
- Image representation, definition of images and volume data sets, illumination, radiometry, multispectral imaging, reflectivities, shape from shading
- Perception of luminance and color, color spaces and transforms, color matching functions, human visual system, color appearance models
- imaging sensors (CMOS, CCD, HDR, X-ray, IR), sensor characterization(EMVA1288), lenses and optics
- spatio-temporal sampling (interpolation, decimation, aliasing, leakage, moiré, flicker, apertures)
- features (filters, edge detection, morphology, invariance, statistical features, texture)
- optical flow ( variational methods, quadratic optimization, Euler-Lagrange equations)
- segmentation (distance, region growing, cluster analysis, active contours, level sets, energy minimization and graph cuts)
- registration (distance and similarity, variational calculus, iterative closest points)
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011
Wedel/Cremers, Stereo Scene Flow for 3D Motion Analysis, Springer 2011
Handels, Medizinische Bildverarbeitung, Vieweg, 2000
Pratt, Digital Image Processing, Wiley, 2001
Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989