MoireDB: Formula-generated Interference-fringe Image Dataset
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917910434283520 |
|---|---|
| author | Matsuo, Yuto Hayamizu, Ryo Kataoka, Hirokatsu Nakamura, Akio |
| author_facet | Matsuo, Yuto Hayamizu, Ryo Kataoka, Hirokatsu Nakamura, Akio |
| contents | Image recognition models have struggled to treat recognition robustness to real-world degradations. In this context, data augmentation methods like PixMix improve robustness but rely on generative arts and feature visualizations (FVis), which have copyright, drawing cost, and scalability issues. We propose MoireDB, a formula-generated interference-fringe image dataset for image augmentation enhancing robustness. MoireDB eliminates copyright concerns, reduces dataset assembly costs, and enhances robustness by leveraging illusory patterns. Experiments show that MoireDB augmented images outperforms traditional Fractal arts and FVis-based augmentations, making it a scalable and effective solution for improving model robustness against real-world degradations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoireDB: Formula-generated Interference-fringe Image Dataset Matsuo, Yuto Hayamizu, Ryo Kataoka, Hirokatsu Nakamura, Akio Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image recognition models have struggled to treat recognition robustness to real-world degradations. In this context, data augmentation methods like PixMix improve robustness but rely on generative arts and feature visualizations (FVis), which have copyright, drawing cost, and scalability issues. We propose MoireDB, a formula-generated interference-fringe image dataset for image augmentation enhancing robustness. MoireDB eliminates copyright concerns, reduces dataset assembly costs, and enhances robustness by leveraging illusory patterns. Experiments show that MoireDB augmented images outperforms traditional Fractal arts and FVis-based augmentations, making it a scalable and effective solution for improving model robustness against real-world degradations. |
| title | MoireDB: Formula-generated Interference-fringe Image Dataset |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.01490 |