Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12866
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dc.contributor.authorKhunarsar P.
dc.contributor.authorBenjathum N.
dc.contributor.authorCharoenpong T.
dc.contributor.authorJariyapongskul A.
dc.date.accessioned2021-04-05T03:21:43Z-
dc.date.available2021-04-05T03:21:43Z-
dc.date.issued2018
dc.identifier.other2-s2.0-85066610304
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/12866-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066610304&doi=10.1109%2fIEECON.2018.8712116&partnerID=40&md5=cb044a6f34f579f120f587ddb562d569
dc.description.abstractTo test the effect of drug on the brain in psychology, brain science and medical science, the rat behavior analysis is a necessary experiment. Morris water maze is a popular neurological model to analyze rat behavior for evaluating rat's ability of study and memory. The ability is evaluated by the time and the distance. However, rat detection is principle process for rat behavior analyzation. In this paper, we proposed a method to detect rat's position in Morris water maze by image processing. Gaussian Mixture Models is used for foreground extraction. The Models is used to compute a multivariate distribution of image sequence. The complexity for automatic rat's behavior analyzation is that the rat has same color with the water. All are white color. To test the performance of the proposed method, five image sequence were used. It consists of 11,655 images. Detection accuracy rate was 83.53%. This method perform effectively even though the rat has same color with the water. © 2018 IEEE.
dc.subjectColor
dc.subjectObject recognition
dc.subjectcomponent
dc.subjectformatting
dc.subjectinsert
dc.subjectstyle
dc.subjectstyling
dc.subjectRats
dc.titleA Method of Swimming Rat Detection in Morris Water Maze by Using Image Processing
dc.typeConference Paper
dc.rights.holderScopus
dc.identifier.bibliograpycitationiEECON 2018 - 6th International Electrical Engineering Congress. (2018)
dc.identifier.doi10.1109/IEECON.2018.8712116
Appears in Collections:Scopus 1983-2021

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