Extending computer vision techniques to recognition problems in 3d volumetric baggage imagery

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.