Scorpion image segmentation system

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dc.contributor.authorJoseph, E.
dc.contributor.authorAibinu, A. M.
dc.contributor.authorSadiq, B. A.
dc.contributor.authorSalau, Bello H.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.date.accessioned2019-08-16T13:23:58Z
dc.date.available2019-08-16T13:23:58Z
dc.date.issued2013
dc.description.abstractDeath as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two nonoverlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1 system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.en_US
dc.identifier.citationJoseph, E., Aibinu, A. M., Sadiq, B. A., Salau, H. B., & Salami, M. J. E. (2013). Scorpion image segmentation system. In IOP Conference Series: Materials Science and Engineering (Vol. 53, No. 1, p. 012055). IOP Publishing.en_US
dc.identifier.uri10.1088/1757-899X/53/1/012055
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/526
dc.language.isoenen_US
dc.publisherIOP Publishingen_US
dc.subjectScorpion imageen_US
dc.subjectSegmentation systemen_US
dc.titleScorpion image segmentation systemen_US
dc.typeArticleen_US
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