• Title/Summary/Keyword: Ellipsoidal Liquid Droplets

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Shape Detection of Ellipsoidal Droplets Using Randomized Hough Transform (Randomized Hough 변환을 이용한 타원형 액적의 형상 검출)

  • Choo, Yeon-Jun;Kang, Bo-Seon
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.27 no.10
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    • pp.1508-1515
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    • 2003
  • In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

Shape Detection of Ellipsoidal Droplets Using Randomized Hough Transform (Randomized Hough 변환을 이용한 타원형 액적의 형상 검출)

  • Choo, Yeon-Jun;Kang, Bo-Seon
    • Proceedings of the KSME Conference
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    • 2003.04a
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    • pp.1783-1788
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    • 2003
  • In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

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