کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
530076 | 869740 | 2013 | 17 صفحه PDF | دانلود رایگان |

We 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.
► Rigid item threat detection in 3D CT baggage imagery.
► CT imagery contains high degree of artefacts which hinder descriptor performance.
► Performance using a variety of local descriptors is compared.
► 3D SIFT orientation invariance methodology weak in this imagery.
► 95% detection rate is achieved using simple local descriptors.
Journal: Pattern Recognition - Volume 46, Issue 9, September 2013, Pages 2420–2436