Bachelor, Master and Doctoral School Projects

Five instances of Liver extracted from MRI data. The black dot indicates a pseudo key point.In this project, we plan to explore methods to match/align shapes extracted from biomedical images, based on shape and texture-based features. By doing so, we will be able to analyze the variations of these structures over time and compare them (…)

DescriptionPrecise human body mesh recovery (or HMR) is a long-standing research topic due to its crucial role for human motion understanding. Recent works proposed very effective solutions for single-frame inputs (as original HMR [1] or more data-efficient [2]) that give the prediction for a body-centered image as well as for body-centered videos (e.g., TCMR [3]).When (…)

DescriptionRecent parametric human body models, such as SMPL [1] or GHUM [2] provide rich and natural yet very sparse prior for realistic body modelling. All parameters have semantic meaning and allow to render diverse set of human bodies in the form of triangular meshes with known topology. Usually, the set of parameters consists of disentangled (…)

In recent years, computer vision techniques for human pose estimation have increasingly become more accurate and robust [1]. However, these techniques are often trained and evaluated on data where humans display “ordinary” poses, such as pedestrians walking in the streets [2]. Several approaches have also been successful in reliably detecting human poses in more “active” (…)

DescriptionModern CNN-based human pose estimators are constructed as regression architectures with human pose prediction as output. This formulation does not naturally involve the confidence of the estimation. Another possible solution is to regress the pose refinement directly via the CNN [2], which gives slight improvement over the ordinary regression-based techniques. However, it still suffers from (…)

Invision system tracking and geo-localizing passengers in a train station using 8 cameras. p { line-height: 115%; text-align: left; orphans: 2; widows: 2; margin-bottom: 0.25cm; direction: ltr; background: transparent }a:link { color: #000080; so-language: zxx; text-decoration: underline } Invision AI develops a multi-camera system to track people and cars. The current system sometimes mixes up persons (…)

For the deployment of Convolutional Neural Networks (CNNs) on battery-powered, energy-constrained edge devices, both weights and activations in a network can be quantized to reduce the energy consumption associated with CNN inference, as low-precision integer arithmetic uses less energy to execute than operations on full-precision floating-point data. However, this incurs a performance/accuracy degradation. Recently, some (…)

If traditional services (loans, consultation, etc.) remain at the heart of the activities of the Cantonal University Library of the Canton of Fribourg (BCU), enhancing and sharing the digital heritage presents new challenges for libraries. In particular, how to rethink the representation of heritage in the digital era? How to create a relationship between physical (…)

An active research field is the prediction of physics mechanisms degrading plasma confinement and performance, eventually leading to disruptions on Tokamaks. Disruptions are the ultimate consequence of highly coupled non-linear plasma physics processes, resulting in an abrupt loss of plasma current and confinement inducing huge electromagnetic forces and thermal loads on Plasma Facing Components and (…)

As in many areas of computer vision, deep networks now deliver state-of-the-art results for delineation tasks, such as finding axons and dendrites in 3D light microscopy images. Most of the existing approaches rely on convolutional networks to extract from images binary masks denoting which voxels belong to neurites and which do not. Unfortunately, they do (…)

Fig 1: Common mistakes made by U-Net (inside orange squares)Current state-of-the art volumetric image segmentation approaches achieve impressive performances. But still, they are not 100% correct and make mistakes as you can see in Figure 1. Therefore we would like to know when a network makes a mistake, so that a human or another model (…)

While modeling people wearing tight-fitting clothes is fast becoming a mature field, handling subjects wearing looser garments remains an open problem when it is to be done in everyday settings where people may hide each other, precise outlines are hard to estimate, and shadows often complicate matters. Our goal is therefore to develop robust and (…)