Analysis of Similarity Structures for Star Identification in Blurred Images
<p id="p2">Astronomical observations often suffer from environmental variations that cause blurring in telescopes, compromising the quality and sharpness of the captured images. To mitigate these effects, this work proposes a methodology for detecting stars in blurred images by developing a mathemat...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
|
| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1218596 |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | <p id="p2">Astronomical observations often suffer from environmental variations that cause blurring in telescopes, compromising the quality and sharpness of the captured images. To mitigate these effects, this work proposes a methodology for detecting stars in blurred images by developing a mathematical model based on probabilistic approaches, exploring the relationship between the light dispersion of stars and a Gaussian distribution. The developed model incorporates two approaches to define the similarity structure: correlation and orthogonality similarity. The results obtained did not show false positives when using orthogonality similarity, achieving a precision of 100%, while the correlation method reached a precision of 95%, highlighting the potential of orthogonality similarity as an effective alternative to identify stellar patterns under blurred conditions. For a dataset with homogeneous star luminosity, an accuracy of 100% was achieved.</p> |
|---|---|
| ISBN: | 978-1-83634-968-6 |
| DOI: | 10.5772/intechopen.1009884 |