ML Anthology

Interpolation-Based Event Visual Data Filtering Algorithms

Marcin Kowalczyk, Tomasz Kryjak
CVPRW 2023 pp. 4056-4064
doi:10.1109/CVPRW59228.2023.00425 /cvprw/2023/kowalczyk2023cvprw-interpolationbased/

Abstract

The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 × 720 and is therefore well suited for implementation in embedded devices.

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Cite

Text

Kowalczyk and Kryjak. "Interpolation-Based Event Visual Data Filtering Algorithms." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00425

Markdown

[Kowalczyk and Kryjak. "Interpolation-Based Event Visual Data Filtering Algorithms." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/kowalczyk2023cvprw-interpolationbased/) doi:10.1109/CVPRW59228.2023.00425

BibTeX

@inproceedings{kowalczyk2023cvprw-interpolationbased,
  title     = {{Interpolation-Based Event Visual Data Filtering Algorithms}},
  author    = {Kowalczyk, Marcin and Kryjak, Tomasz},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {4056-4064},
  doi       = {10.1109/CVPRW59228.2023.00425},
  url       = {https://mlanthology.org/cvprw/2023/kowalczyk2023cvprw-interpolationbased/}
}