Flitton, Greg T.Breckon, Toby P.Megherbi, Najla2020-03-032020-03-032013-02-16Flitton GT, Breckon TP, Megherbi Baouallagui N. (2013) A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery. Pattern Recognition, Volume 46, Issue 9, September 2013, pp. 2420-24360031-3203https://doi.org/10.1016/j.patcog.2013.02.008https://dspace.lib.cranfield.ac.uk/handle/1826/15213We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/CT baggage scanThreat detectionObject recognition3D feature descriptorsCT object SIFTArticle