Lectures:
Detecting edges in images. Gradient, and zero-crossing methods. Parametric edge models.
Canny edge detector.
Thresholding. Optimal threshold selection.
Image segmentaion based on region growing and splitting.
Edge linking. Heuristic edge following. Method of clustering in the space of parameters.
Representing boundaries and areas: Encoding boundaries using line segments and curves. Line and curve fitting. Representing areas.
Detecting feature points (corners).
Measuring objects. Selection and computation of features for pattern recognition. Evaluating the efficiency and optimisation of the set of selected features.
Pattern recognition based on classification. Discriminant functions and etalons.
Probablistic approach to determining the discriminant functions.
Using neural networks for pattern recognition.
Reconstructing a scene from its two or more images.
Absolute and relative camera calibration and reconstruction.
Analysis of time-varying images. Tracking objects in image sequences.
Projects:
As a project, the students will carry out a simple recogniser of two-dimensional geometrical shapes.
Computer labs:
During the exercises, the students work out a series of practical tasks (detecting edges, areas, corners, computing features, etalon-based classification). The tasks are prepared in the form of templates (pre-prepared programs) into which the sudents fill their own source code. In this way, they can focus on substantial and interesting issues. Furthermore, examples of reconstructing 3D scenes are presented.
Detecting edges in images. Gradient, and zero-crossing methods. Parametric edge models.
Canny edge detector.
Thresholding. Optimal threshold selection.
Image segmentaion based on region growing and splitting.
Edge linking. Heuristic edge following. Method of clustering in the space of parameters.
Representing boundaries and areas: Encoding boundaries using line segments and curves. Line and curve fitting. Representing areas.
Detecting feature points (corners).
Measuring objects. Selection and computation of features for pattern recognition. Evaluating the efficiency and optimisation of the set of selected features.
Pattern recognition based on classification. Discriminant functions and etalons.
Probablistic approach to determining the discriminant functions.
Using neural networks for pattern recognition.
Reconstructing a scene from its two or more images.
Absolute and relative camera calibration and reconstruction.
Analysis of time-varying images. Tracking objects in image sequences.
Projects:
As a project, the students will carry out a simple recogniser of two-dimensional geometrical shapes.
Computer labs:
During the exercises, the students work out a series of practical tasks (detecting edges, areas, corners, computing features, etalon-based classification). The tasks are prepared in the form of templates (pre-prepared programs) into which the sudents fill their own source code. In this way, they can focus on substantial and interesting issues. Furthermore, examples of reconstructing 3D scenes are presented.