Constructing and Evaluating Complex Event-based Datasets for Increasing Performance of Instance Segmentation Models

Bachelor Thesis (2022)
Author(s)

A.D. Manolache (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Nergis Tömen – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Ombretta Strafforello – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Xin Liu – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Luciano Siebert – Graduation committee member (TU Delft - Interactive Intelligence)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 Alexandru-Dragos Manolache
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Alexandru-Dragos Manolache
Graduation Date
22-06-2022
Awarding Institution
Delft University of Technology
Project
['CSE3000 Research Project']
Programme
['Computer Science and Engineering']
Related content

GitHub code repository

https://github.com/alexmanoo/dvs_datasets_transforms
Faculty
Electrical Engineering, Mathematics and Computer Science
Reuse Rights

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Abstract

Event-based cameras represent a new alternative to traditional frame based sensors, with advantages in lower output bandwidth, lower latency and higher dynamic range, thanks to their independent, asynchronous pixels. These advantages prompted the development of computer vision methods on event data in the last decade, however event-based datasets are still in early stages in terms of size and complexity compared to normal datasets (e.g. ImageNet). This paper explores event data augmentation by superimposing two existing event datasets (N-MNIST and N-Caltech101) and by adding uniform noise. It shows that training an instance segmentation model on noisy datasets does not improve its performance, but the amount and type of noise added in the background decreases the performance of such model.

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