IGSF IMA 4509
At the end of this teaching unit, students will be able
- To model an inverse imaging problem under a mathematically well-posed form via a deterministic or stochastic regularization approach, and to solve it using companion optimization methods.
- To specify & use core techniques for solving basic low-level image & video analysis problems, including restoration, multiscale description, point of interest detection, segmentation, calibration & 3D scene reconstruction, motion analysis & tracking.
- To design & conduct an experimental performance study of an image understanding technique, relying on dedicated software tools such as the industry-standard Matlab(R) platform or the open-source OpenCV library.
- To write a synthetic and reasoned critical analysis of a scientific article dealing with an image understanding or computer vision issue.
Visual feature extraction; denoising, enhancement & restoration; segmentation & grouping; motion estimation & tracking; shape analysis.
> Digital image & video analysis: imaging chain, paradigms and models
> Still image analysis
- Advanced image filtering & feature detection
• Interest point/line detection: corners, ridges and valleys
• Non-local image filtering: bilateral filters, Non-Local means, patch-based image denoising
- Gray-level mathematical morphology
• basic Euclidean & geodesic operators
• morphological filtering
• morphological segmentation
- Inverse problems in image analysis: deterministic & stochastic regularization
-Deterministic image segmentation: variational methods & graph cuts
• Active contours: parameterized models, level set methods
• Active regions: the Mumford-Shah model, deterministic & statistical region competition, multi-feature variational segmentation
- Scale-space & PDE image filtering
- Bayesian methods & Markov Random Fields
> Multi-view image analysis
- Stereovision & multi-view 3D scene/object reconstruction
> Video analysis
- Motion measurement & optical flow estimation
- Spatio-temporal segmentation & object tracking
The assessment pattern involves 3 components: continuous evaluation via homework on selected topics (”coursework”) (CW), lab assignments (L), and a two-student group written final exam (E). The final grade is a weighted average of individual component grades. The 2nd session consists of a study with an oral defense (O).
-1st session = Weighted Average (CW, L, E)) (S1)
-2nd session = O (S2)
-Final grade = Max (SE1, SE2)