Summary of Xami — a Benchmark Dataset For Artefact Detection in Xmm-newton Optical Images, by Elisabeta-iulia Dima et al.
XAMI – A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images
by Elisabeta-Iulia Dima, Pablo Gómez, Sandor Kruk, Peter Kretschmar, Simon Rosen, Călin-Adrian Popa
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a dataset and machine learning methods to detect and mask artefacts in astronomical observations, which are crucial for advancing scientific studies. The authors create a dataset of images from the XMM-Newton space telescope Optical Monitoring camera, annotating 1000 images with different types of artefacts. They train automated models using this data, combining convolutional neural networks (CNNs) and transformer-based models for instance segmentation. The proposed method and dataset provide a reproducible baseline for artefact detection in astronomical observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps astronomers detect fake light in space images, which can mess up scientific research. The authors make a special dataset with 1000 images of different kinds of fake light and teach machines to spot them. They use a mix of two types of machine learning models: CNNs and transformers. This makes their method better for finding fake light in space pictures. |
Keywords
» Artificial intelligence » Instance segmentation » Machine learning » Mask » Transformer
