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This research presents a hybrid approach integrating computer vision and deep learning techniques for pattern recognition in dense astronomical star fields. Using the Sloan Digital Sky Survey (SDSS) dataset, the study focuses on improving accuracy, reducing false detections, and enabling efficient real-time analysis. The proposed model combines preprocessing, segmentation, and convolutional neural networks (CNNs) to achieve robust performance in complex visual environments.
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Cite Article:
"AI-Driven Real-Time Pattern Recognition in Dense Star Fields Using Hybrid Computer Vision and Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b16-b17, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604136.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator