Computer vision and augmented reality

This course covers the notions of calibration, interest point detection (in mono or multi-resolution), matching (global and local) and tracking. In addition, you will learn about the well-known SIFT (Scale Invariant Feature Transform) approach and a classical KLT (Kanade-Lucas-Tomasi) tracking approach.

This part is composed of 2 reversed classroom lessons in order to allow the learner to be more active in his learning. Then, 4 practical works illustrate the notions of detection and matching discussed in class in order to build a mosaic of images. This subject will be evaluated via an online course questionnaire and an exam on paper as well as a grade for the practical work. This allows a continuous evaluation of the acquired knowledge.

To have followed the second year course Image, Modeling and Rendering or to have notions of image processing and segmentation.