Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/52412
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dc.contributor.authorPhawis Thammasornen_US
dc.contributor.authorSukitta Boonchuen_US
dc.contributor.authorAram Kawewongen_US
dc.date.accessioned2018-09-04T09:25:03Z-
dc.date.available2018-09-04T09:25:03Z-
dc.date.issued2013-12-01en_US
dc.identifier.other2-s2.0-84897748147en_US
dc.identifier.other10.1109/ICIP.2013.6738845en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84897748147&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/52412-
dc.description.abstractA novel method that automatically counts number of objects piled or stacked to each other in mobile device image is proposed. Distinct from other methods, the proposed method automatically determines the size and shape and detects the template of unknown to-be-counted objects. Such template is then used to perform fast template matching all over the input image to count the number of the same objects. The similarity function for determining the detected object has been proposed. The evaluation on speed and accuracy was performed by testing on images of various construction materials captured from on real site by the Android smart phone. The result suggests that the proposed method outperforms all baselines including the recent lattice detection based approach. The computation time is sufficiently fast for real-time use in mobile devices. It can also efficiently deal with the problem of information loss (i.e., lost edges) due to various unpredicted lighting condition. In term of time, the comparison of counting time was also done between the method and human. © 2013 IEEE.en_US
dc.subjectComputer Scienceen_US
dc.titleReal-time method for counting unseen stacked objects in mobileen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedingsen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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