COURSES

Goals

Objectives: The student is able to assess problems and chose appropriate methods to acquire information from digital images. He can justify his choice based on his theoretical knowledge and experience gained by practical work. His choice of methods is based on requirements of the practical problem and design properties of the technical system to be implemented. Competences: - Assessing if computer vision methods can be used to solve a given technical problem. - Understanding the functionality of different computer vision methods in practical problems. - The ability to independently apply appropriate methods to practical problems. - The ability to implement and practically test different algorithms for problems like object recognition and tracking. - Knowing how to find appropriate methods in the literature when faced with a new problem.

Curriculum

Introduction - Introduction to computer vision. - Practical applications of computer vision. - Introduction to Matlab. Image formation - Photometrical models and optics. - Human vision. - Camera calibration. - Projective geometry and invariances. 2-D image processing - Acqusition and representation of digital images. - Digital filters and edge detection. - Image segmentation and region detection. - Morphology. - Color and histogramms. - Pattern matching. - Application: Contrast adjustment. 3-D computer vision - Stereo vision; calibration, correspondence problem and triangulation. - Range images. - Reconstruction of geommetrical models. - Application: modeling cultural heritage. Motion detection and tracking - Optical flow. - Motion approximation. - Object tracking and Kalman filter. - Aplication: human head tracking. Object recognition - Issues in object recognition and computational mechanisms. - View-based approaches. - Hypotheses generation and verification. - Recognition by parts. - Application: face recognition. Deep neural networks - Neural network architectures. - Training of deep neural networks. - Convolutional neural networks.