Abstract:
We investigate the application of computer vision techniques to rigid object recognition
in Computed Tomography (CT) security scans of baggage items. This imagery
is of poor resolution and is complex in nature: items of interest can be imaged in
any orientation and copious amounts of clutter, noise and artefacts are prevalent.
We begin with a novel 3D extension to the seminal SIFT keypoint descriptor
that is evaluated through specific instance recognition in the volumetric data. We
subsequently compare the performance of the SIFT descriptor against a selection of
alternative descriptor methodologies. We demonstrate that the 3D SIFT descriptor
is notably outperformed by simpler descriptors which appear to be more suited for
use in noise and artefact-prone CT imagery.
Rigid object class recognition in 3D volumetric baggage data has received little
attention in prior work. We evaluate contrasting techniques between a traditional
approach derived from interest point descriptors and a novel technique based on
modelling of the primary components of the primate visual cortex.
We initially demonstrate class recognition through the implementation of a codebook
approach. A variety of aspects relating to codebook generation are investigated
(codebook size, assignment method) using a range of feature descriptors. Recognition
of a number of object classes is performed and results from this show that the
choice of descriptor is a critical aspect.
Finally, we present a unique extension to the established standard model of the
visual cortex: a volumetric implementation. The visual cortex model comprises a
hierarchical structure of alternating simple and complex operations that has demonstrated
excellent class recognition results using 2D imagery. We derive 3D extensions
to each layer in the hierarchy resulting in class recognition results that signficantly
outperform those achieved using the earlier traditional codebook approach.
Overall we present several novel solutions to object recognition within 3D CT
security images that are supported by strong statistical results.
