FCN for Semantic Image Segmentation achieving 68.5 mIoU on PASCAL VOC

Use deep learning model to produce a pixel-by-pixel classification of images and identify road for autonomous driving vehicles

Código implementado en el desarrollo del modelo UNet, utilizado para el proyecto sobre la segmentación del miocardio en MRI (imágenes de resonancias magnéticas). Realizado por Ricardo Espinoza y Nicolás Becerra, junto con la colaboración de Francisca Cona.

The goal is to segment instances of microvascular structures, including capillaries, arterioles, and venules, to in automating the segmentation of microvasculature structures as it will improve researchers' understanding of how the blood vessels are arranged in human tissues.

The goal is to segment instances of microvascular structures, including capillaries, arterioles, and venules, to in automating the segmentation of microvasculature structures as it will improve researchers' understanding of how the blood vessels are arranged in human tissues.

Computer vision project: In the name of Deep Learning - as part of the Computer Vision course @ KU Leuven

MATLAB implementation of popular image segmentation algorithms