3D object classification in baggage computed tomography imagery using randomised clustering forests

We investigate the feasibility of a codebook approach for the automated classification of threats in pre-segmented 3D baggage Computed Tomography (CT) security imagery. We compare the performance of five codebook models, using various combinations of sampling strategies, feature encoding techniques and classifiers, to the current state-of-the-art 3D visual cortex approach [1]. We demonstrate an improvement over the state-of-the-art both in terms of accuracy as well as processing time using a codebook constructed via randomised clustering forests [2], a dense feature sampling strategy and an SVM classifier. Correct classification rates in excess of 98% and false positive rates of less than 1%, in conjunction with a reduction of several orders of magnitude in processing time, make the proposed approach an attractive option for the automated classification of threats in security screening settings

Investigating Existing Medical CT Segmentation Techniques within Automated Baggage and Package Inspection

3D Computed Tomography (CT) image segmentation is already well established tool in medical research and in routine daily clinical practice. However, such techniques have not been used in the context of 3D CT image segmentation for baggage and package security screening using CT imagery. CT systems are increasingly used in airports for security baggage examination. We propose in this contribution an investigation of the current 3D CT medical image segmentation methods for use in this new domain. Experimental results of 3D segmentation on real CT baggage security imagery using a range of techniques are presented and discussed. 1

Object Classification in 3D Baggage Security Computed Tomography Imagery using Visual Codebooks

We investigate the performance of a Bag of (Visual) Words (BoW) object classification model as an approach for automated threat object detection within 3D Computed Tomography (CT) imagery from a baggage security context. This poses a novel and unique challenge for rigid object classification within complex and cluttered volumetric imagery. Within this context it extends the BoW model to 3D transmission imagery (X-ray CT) from its conventional application in 2D reflectance (photographic) imagery. We explore combinations of four 3D feature descriptors (Density Histogram (DH), Density Gradient Histogram (DGH), Scale Invariant Feature Transform (SIFT) and Rotation Invariant Feature Transform (RIFT)), three codebook assignment methodologies (hard, kernel and uncertainty) and seven codebook sizes. Optimal performance is achieved using the DH and DGH descriptors in conjunction with an uncertainty assignment methodology. Successful detection rates in excess of 97% for handguns and 89% for bottles and false-positive rates of approximately 2–3% are achieved. We demonstrate that the underlying imaging modality and the irrelevance of illumination and scale invariance within the transmission imagery context considered here result in the favourable performance of simpler density histogram descriptors (DH, DGH) over 3D extensions of the well-established SIFT and RIFT feature descriptor approaches

Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery

This study presents a novel method for liquid detection within three-dimensional (3D) computed tomography (CT) baggage inspection imagery. Liquid detection within airport security is currently of significant interest due to security threats associated with liquid explosives. In this paper, we propose a robust technique based on the automatic identification of universal geometric properties of liquids within 3D space. The proposed approach is based on two stages of geometric fitting. First, we identify the 3D plane which fits to the horizontally oriented surface of the liquid recognising the universal self-levelling property of liquids in any given container. Second, we conduct two-dimensional shape analysis to highlight the shape of the liquid surface at a given level within the container using a least squares elliptical fitting approach. The proposed approach relies on the fact that occurrences of such perfectly aligned horizontal planes within a 3D CT security baggage scan are generally unlikely. Occurrences of such instance are thus indicative of liquid presence. Our results, over an extended set of complex test examples, confirm a liquid detection rate of 85–98% with a moderate processing time. Furthermore, as this proposed approach is based purely on the geometric properties of liquids and robust geometrical shape detection, this methodology is intrinsic to the 3D nature of the resulting CT data and not dependent on any exemplar training imagery

A Comparison of Classification Approaches for Threat Detection in CT based Baggage Screening

Computed Tomography (CT) based baggage security screening systems are of increasing use in transportation security. The ability to automatically identify potential threat item is a key aspect of current research in this area. Here we present a comparison of varying classification approaches for the automated detection of threat objects in cluttered 3D CT imagery from such security screening systems. By combining 3D medical image segmentation techniques with 3D shape classification and retrieval methods we compare five varying final classification stage approaches and present significant performance achievements in the automated detection of specified exemplar items. Index Terms — aviation security, 3D medical image segmentation, 3D Zernike descriptors, histogram of shape index, automated classification, 3D object recognition. I