Relevance Detection of Unknown Classes through Cluster Distances: Based on Statistical Distance Measures in Feature Space

In the open world, machine learning (ML) models can encounter a multitude of unknown or novel classes. In a surveillance, safety, or security use case, unknown samples can pose potential threats that are hard to detect since those samples have never been trained on. At the same time, most of the unknowns that will be encountered by a...

Less Machine (=) More Vision: Approaches towards Practical and Efficient Machine Vision with Applications in Face Analysis

Machines that interact with humans can do so better if they can also visually understand us, but they have limited resources to do so. The main topic of this dissertation is contrasting the use of resources by machine vision systems against the accuracy obtained by them. This thesis focuses on reducing the need for data, memory, and computation...

Transferring a Segmentation Task Between Real and Synthetic Data

In the field of ecology, camera traps are important tools to collect information on the wildlife of certain areas. The problem that arises with many camera traps is that they can collect more images than a human can realistically go trough all by themselves. To help classify these images computer vision is proposed as an alternative to manual...