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Clustering and detection of good and bad rail line anchors from images

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dc.contributor.author Islam, Samiul
dc.contributor.author Khan, Rubayat Ahmed
dc.date.accessioned 2017-01-04T04:43:54Z
dc.date.available 2017-01-04T04:43:54Z
dc.date.issued 2016-06
dc.identifier.citation Islam, S., & Khan, R. A. (2015). Clustering and detection of good and bad rail line anchors from images. Paper presented at the 2015 18th International Conference on Computer and Information Technology, ICCIT 2015, 222-226. doi:10.1109/ICCITechn.2015.7488072 en_US
dc.identifier.isbn 978-146739930-2
dc.identifier.uri http://hdl.handle.net/10361/7501
dc.description This conference paper was presented in the International Conference on Computer and Information Technology, ICCIT 2015; Military Institute of Science and Technology (MIST)Mirpur CantonmentDhaka; Bangladesh; 21 December 2015 through 23 December 2015 [© 2015 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ICCITechn.2015.7488072 en_US
dc.description.abstract Absence of railway anchors/fasteners is a serious concern as it might lead to severe consequences such as train derailments. Hence regular inspection is an obligation to ensure safety. The third world countries choose the inspection process to be non-automatic where a trained operator moves along the rail line boarding a motor trolley checking for visual anomalies. In the previous research [1], an automatic system was proposed to overcome the cons of the running manual technique by using image processing. Two feature detection algorithms - Shi Tomasi and Harris Stephen - were used and an accuracy of 83.55% was achieved. This research presents an upgraded version of the previous work by introducing Neural Network. The addition of NN has not only speeded up the detection process but increased the accuracy significantly to approximately 93.86%. en_US
dc.language.iso en en_US
dc.publisher © 2015 Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.uri http://ieeexplore.ieee.org/document/7488072/
dc.subject Feature detection en_US
dc.subject Feature extraction en_US
dc.subject Neural networks en_US
dc.subject Training en_US
dc.title Clustering and detection of good and bad rail line anchors from images en_US
dc.type Conference paper en_US
dc.description.version Published
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.identifier.doi http://dx.doi.org/10.1109/ICCITechn.2015.7488072


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