Uncertainty estimation and its applications in computer vision

Abstract: Deep learning has become the common practice for most computer vision tasks due to being state-of-the-art in both accuracy and runtime. It has not only revolutionized machine learning and computer vision, but also moved us a big step closer to artificial intelligence. Due to its success, besides academia, it has already been part of many industrial and clinical solutions; from autonomous driving to Instagram’s cyberbullying detection, from Twitter’s tweet curation to Heineken’s data-driven marketing, from music generation to augmenting/replacing radiologists to detect cancer in Computed Tomography scans. While deployment of deep learning approaches is straight-forward as better ones are being developed, for safety-critical applications a big challenge remains: estimating uncertainty. In autonomous systems like self-driving cars, it is of great importance that the system knows when it does not know e.g. heavy rain obscures the vision,
the car was trained for highway but is now at Arc de Triomphe in Paris or a koala runs to the street due to a bush fire. Uncertainty estimation not only enables us to quantify the reliability of a decision coming from a system, but when modeled fully, also enables us to solve nondeterministic tasks with more than one possible outcome, such as future prediction: an important aspect of human intelligence. Once we have a reasonable estimation for the uncertainty of a subsystem, another challenge is to make good use of this new data modality by propagating it properly through chains of subsystems to improve the result of the whole system.
This thesis starts with a general presentation of the value of deep learning in medical image segmentation.
Then, it continues by equipping modern convolutional neural networks with uncertainty estimation in show-cases of optical flow and future localization.
Finally, it uses the uncertainty estimation to improve network predictions in tracking cell nuclei tagged with a dynamic protein and future captioning

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Universität Freiburg, Dissertation, 2021

Classification and Topics

• Keyword: Uncertainty ; Computer vision ; Deep Learning ; Maschinelles Sehen ; Maschinelles Lernen

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2021
• Creator: Çiçek, Özgün
• Contributor: Brox, Thomas ; Albert-Ludwigs-Universität Freiburg. Fakultät für Angewandte Wissenschaften

Further information

• DOI: 10.6094/UNIFR/194779
• URN: urn:nbn:de:bsz:25-freidok-1947798
• Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 11:03 AM CEST