{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T06:53:30Z","timestamp":1729666410315,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,10]]},"DOI":"10.1109\/ccst.2016.7815717","type":"proceedings-article","created":{"date-parts":[[2017,1,17]],"date-time":"2017-01-17T03:56:01Z","timestamp":1484625361000},"page":"1-7","source":"Crossref","is-referenced-by-count":16,"title":["Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines"],"prefix":"10.1109","author":[{"given":"Thomas W.","family":"Rogers","sequence":"first","affiliation":[]},{"given":"Nicolas","family":"Jaccard","sequence":"additional","affiliation":[]},{"given":"Emmanouil D","family":"Protonotarios","sequence":"additional","affiliation":[]},{"given":"James","family":"Ollier","sequence":"additional","affiliation":[]},{"given":"Edward J.","family":"Morton","sequence":"additional","affiliation":[]},{"given":"Lewis D.","family":"Griffin","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Smote: synthetic minority over-sampling technique","volume":"16","author":"chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"ref11","first-page":"1","article-title":"C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling","author":"drummond","year":"2003"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3233\/IDA-2002-6504","article-title":"The class imbalance problem: A systematic study","volume":"6","author":"japkowicz","year":"2002","journal-title":"Intelligent Data Analysis"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.17"},{"key":"ref14","article-title":"Return of the devil in the details: Delving deep into convolutional nets","volume":"abs 1405 3531","author":"chatfield","year":"2014","journal-title":"CoRR"},{"key":"ref15","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IST.2014.6958504"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1049\/cp.2015.1762"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1272582.1272606"},{"key":"ref19","first-page":"98 470n","article-title":"Tackling the x-ray cargo inspection challenge using machine learning","author":"jaccard","year":"2016","journal-title":"SPIE Defense Security and Sensing International Society for Optics and Photonics"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.2495\/SAFE050411","article-title":"Using threat image projection data for assessing individual screener performance","volume":"82","author":"hofer","year":"2005","journal-title":"WIT Transactions on The Built Environment"},{"key":"ref3","first-page":"317","article-title":"The impact of image based factors and training on threat detection performance in x-ray screening","author":"schwaninger","year":"2008","journal-title":"Proceedings of the 3rd International Conference on Research in Air Transportation"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IPTA.2012.6469523"},{"article-title":"Threat image projection-an overview","year":"2003","author":"mitckes","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1117\/12.766432"},{"key":"ref7","first-page":"89010b","article-title":"Radon transform based automatic metal artefacts generation for 3d threat image projection","year":"2013","journal-title":"Proc of SPIE Security + Defence International Society for Optics and Photonics"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CCST.2007.4373478"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/435439a"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0084217"},{"key":"ref20","first-page":"1","article-title":"Detection of concealed cars in complex cargo x-ray imagery using deep learning","volume":"abs 1606 0","year":"2016","journal-title":"CoRR"},{"article-title":"Using deep learning on x-ray images to detect threats","year":"0","author":"jaccard","key":"ref22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2014.6918699"},{"key":"ref24","article-title":"Rapid GPU-based simulation of x-ray transmission, scatter, and phase measurements for threat detection systems","author":"gong","year":"2016","journal-title":"International Society for Optics and Photonics"},{"key":"ref23","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"abs 1409 1556","author":"simonyan","year":"2014","journal-title":"CoRR"}],"event":{"name":"2016 International Carnahan Conference on Security Technology (ICCST)","start":{"date-parts":[[2016,10,24]]},"location":"Orlando, FL, USA","end":{"date-parts":[[2016,10,27]]}},"container-title":["2016 IEEE International Carnahan Conference on Security Technology (ICCST)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7799553\/7815669\/07815717.pdf?arnumber=7815717","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T16:41:29Z","timestamp":1568738489000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7815717\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/ccst.2016.7815717","relation":{},"subject":[],"published":{"date-parts":[[2016,10]]}}}