Application of a histogram modification algorithm to the processing of raindrop images

Sivasubramanian, Nithya A.

doi:10.1117/1.2899101

Cited by 3 publications

(3 citation statements)

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“…While there are differences in the D m versus z behavior for the edge detection algorithms presented in this work, the behavior among these algorithms is quite similar when compared to different algorithmic approaches. (plot (c) obtained from Sivasubramanian and Saylor [35]).…”

Section: Measurement Errormentioning

confidence: 99%

“…The flatness of the D m versus z plots presented here shows that, in general, the average measured diameter is not significantly influenced by the size of the depth of field. Hence, there is no trade-off between dof and the accuracy of measuring the diameter, which is usually the case in these applications [34,35].…”

Section: Measurement Errormentioning

confidence: 99%

“…Image segmentation processes are broadly classified into the following three groups [32,33]: a) thresholding or clustering techniques; b) region growing and c) edge detection techniques. Thresholding methods have been investigated in our earlier work and will not be discussed here [34,35]. Region growing techniques have a high degree of complexity associated with them and were not considered to be practical for this application.…”

Section: Introductionmentioning

confidence: 99%

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Edge detection methods applied to the analysis of spherical raindrop images

Saylor

Sivasubramanian

2007

Appl. Opt.

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Optical imaging of raindrops provides important information on the statistical distribution of raindrop size and raindrop shape. These distributions are critical for extracting rainfall rates from both dual- and single-polarization radar signals. A large number of raindrop images are required to obtain these statistics, necessitating automatic processing of the imagery. The accuracy of the measured drop size depends critically on the characteristics of the digital image processing algorithm used to identify and size the drop. Additionally, the algorithm partially determines the effective depth of field of the camera/image processing system. Because a large number of drop images are required to obtain accurate statistics, a large depth of field is needed, which tends to increase errors in drop size measurement. This trade-off between accuracy and depth of field (dof) is also affected by the algorithm used to identify the drop outline. In this paper, eight edge detection algorithms are investigated and compared to determine which is best suited for accurately extracting the drop outline and measuring the diameter of an imaged raindrop while maintaining a relatively large depth of field. The algorithm which overall gave the largest dof along with the most accurate estimate of the size of the drop was the Hueckel algorithm [J. Assoc. Comput. Mach. 20, 634 (1973)].

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Section: Measurement Errormentioning

confidence: 99%

Section: Measurement Errormentioning

confidence: 99%