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A Self-Construction of Automatic Crescent Detection Using Haar-Cascade Classifier and Support Vector Machine

Journal of physics. Conference series, 2024-03, Vol.2734 (1), p.012007 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2734/1/012007

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  • Title:
    A Self-Construction of Automatic Crescent Detection Using Haar-Cascade Classifier and Support Vector Machine
  • Author: Muztaba, R ; Malasan, H L ; Djamal, M
  • Subjects: Algorithms ; Classifiers ; Computer vision ; Moon ; Object recognition ; Support vector machines
  • Is Part Of: Journal of physics. Conference series, 2024-03, Vol.2734 (1), p.012007
  • Description: Developing an automatic detection method based on computer vision applied to the moon crescent is an innovative concept that can be further developed. This program will be highly useful for observers during the Moon crescent observation because it can help them recognize objects quickly. This paper proposes an automatic crescent moon detection method based on visual mechanisms and training using the Cascade Classifier algorithm. The stages of this method consist of building Haar structural features, extracting feature samples using Haar structural features, and training 981 images consisting of 654 positive images and 327 negative images using the Cascade Classifier. The results show that the crescent moon detection performance is quite good at detecting the crescent Moon. The developed program can recognize crescent moon objects, although it is limited to relatively large lunar illumination in the range of greater than 10% to less than 50%. Furthermore, our program can be applied in real-time situations.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2734/1/012007
  • Source: IOP Publishing Free Content
    GFMER Free Medical Journals
    ProQuest Central
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