The object of this course on Computer Vision & Machine Intelligence is to present fundamentals on statistical pattern recognition techniques, as well as the bases of applied digital image analysis.

Machine Intelligence & Pattern Recognition: feature extraction, clustering, Principal Component Analysis, Gaussian models, Feed-Forward Artificial Neural Networks, Deep Neural Networks, Dynamic Time Warping, Hidden Markov Models.

Computer Vision & Image Analysis: low-level image processing (erosion, dilatation, …); image understanding (analysis, denoising, segmentation, watershed, active contours).

Many examples of related research projects can be find on the project web page of the TCTS lab, as well as on the Numediart web page.