Computer vision

Basics of computer vision: image formation and geometry, filters/contours, visual features.
Classification, object detection and semantic segmentation: principles of CNNs, R-CNN/YOLO architectures, U-Net, Mask R-CNN, etc. Generative models for image processing and analysis (VAEs, GANs, etc.).
Generative models for image processing and analysis (VAEs, GANs, etc.).
Other state-of-the-art approaches.
Realization of a project implementing the concepts studied.

Basics of computer vision: image formation and geometry, filters/contours, visual features.
Classification, object detection and semantic segmentation: principles of CNNs, R-CNN/YOLO architectures, U-Net, Mask R-CNN, etc. Generative models for image processing and analysis (VAEs, GANs, etc.).
Generative models for image processing and analysis (VAEs, GANs, etc.).
Other state-of-the-art approaches.
Realization of a project implementing the concepts studied.