Doctoral School Projects
Master Thesis or Doctoral School Projects
- 3D Medical Image Analysis beyond Voxels
- Skeleton based Action Recognition using Learnable Textual Inputs
- STReAKS: Synthetic sTreak Rendering for sAtellite Kinematics and Surveillance
- Waste Detection in real-time: Object detection of waste type from the fall of waste in a waste cell (CVLab x Wasteflow)
- Object detection and classification of waste type and mass assessment as it moves along a conveyor belt (CVLab x Wasteflow)
- Sensor fusion deep learning approach to the problem of dangerous waste detection in case of partially or fully hidden objects (CVLab x Wasteflow)
- Applied Deep Learning for Computer Aided Engineering
- Spacecraft Dataset Development for Unseen and Occluded Targets
- Exploring the Future of Classical Music
- Quantized Neural Networks for Space Applications
- Learning silhouette appearance to improve multi-people tracking [master]
- Deep Learning for Nuclear Fusion
- Promoting Connectivity of Linear Structures in 3D Microscopy Images
- Modeling People and their Clothes in Crowded Scenes
- Multi-task Active Learning
- Physically Constrained Deep Networks
For further project offers, please contact members of the CVLAB directly.
Semester Projects (Bachelor and Master)
Most of the offered semester projects can be rephrased for a thesis and vice versa. Please contact us directly.
Administrative
Semester Projects (Bachelor and Master)
SIN and SSC students do one semester project during their Bachelor studies and one semester project during their Master studies.
Semester projects can be done in groups of two students.
Semester projects are worth 8 credits for Bachelor and 12 credits for Master.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
Oral defense: within two weeks of the hand-in date.
Master Thesis Projects
Master Thesis Projects are started once the complete master program is finished and all the credits have been obtained.
Projects for SSC and SIN students should last 4 months at the EPFL or 6 months in the industry or in another University.
Master Thesis Projects must be done individually.
Master Thesis Projects are worth 30 credits.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
Additional information

Through this project, you will be developing a solution for a real-world application that will be used in the future within WasteFlow service and will help optimize recycling.

Through this project, you will be developing a solution for real-world application that will be used in the future within WasteFlow service and will help optimize recycling.

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 (…)

The goal of this project is to develop a tool that allows the insertion of realistic synthetic observations of space objects into astronomical images.

As described by Lionel Esparza, French musicologist, musician and radio producer (France Musique) in 2021, classical music is in decline. The reasons are various such as ageing of the audience, cultural shift and mismatch. At the same period, two major observations arose from the discussions between the Verbier classical music festival and the EPFL+ECAL Lab: (…)

Deep learning based approaches can be used for a wide range of space applications such as on-board data processing for observation satellite and collision prevention, spacecraft rendezvous, etc. Unfortunately, the deep learning models are very computational intensive and require huge amount of resources and power consumption. In recent years, some techniques like quantization, pruning and (…)

Computer Aided Engineering (CAE) is at the core of modern industrial engineering and manufacturing. However, the current CAE applications suffer from significant time and human resource expenses. Our goal is to leverage deep learning techniques to automate the CAE process and reduce the R&D costs for the industry.

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 (…)

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 (…)

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 (…)

DescriptionDifferent visual tasks are often strongly and obviously correlated. For instance, having surface normals simplifies estimating the depth of an image, knowing segmentation could help detect objects, etc. Our intuition implies the existence of a special structure among visual tasks. Extracting this structure would allow us to seamlessly reuse supervision among related tasks or solve (…)

Many practical continuous minimization problems, such as aerodynamic optimization, are not amenable to gradient-based optimization methods because derivatives cannot be computed directly. We recently showed that it is possible to train a Neural Network regressor as a proxy to the numerical simulator and optimize the proxy function via Gradient-Descent. [ICML 2018 Paper]. For example, we (…)