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Computer vision applications for coronagraphic optical alignment and image processing
Dmitry Savransky1, Sandrine J Thomas, Lisa A Poyneer
1Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA. savransky1@llnl.gov
Applied Optics
|May 15, 2013
Summary
Automated image processing using computer vision techniques aids coronagraphic systems. Feature extraction and clustering methods improve alignment and calibration for instruments like the Gemini Planet Imager.
Area of Science:
- Astronomy and Astrophysics
- Computer Vision
- Image Analysis
Background:
- Coronagraphic systems demand high precision optical alignment.
- Automated image processing offers significant benefits for these systems.
Purpose of the Study:
- To apply computer vision and image analysis techniques to coronagraphic systems.
- To demonstrate automated alignment and calibration using feature extraction, clustering, and search algorithms.
Main Methods:
- Feature extraction and clustering for automated system alignment.
- Development of a search algorithm for identifying regular features in science images.
- Implementation and testing on the Gemini Planet Imager instrument.
Main Results:
- Successful application of feature extraction and clustering for alignment tasks.
- Demonstrated efficacy of the search algorithm in calibration and data processing.
- Validation of techniques through operational results on the Gemini Planet Imager.
Conclusions:
- Computer vision and image analysis techniques are effective for automating coronagraphic system alignment and calibration.
- These methods enhance precision and efficiency in astronomical instrumentation.
- The implemented algorithms provide practical solutions for operational challenges in observatories.
