A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery

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.