Najla Megherbi

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.

An experimental survey of metal artefact reduction in computed tomography.

We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-the-art Metal Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of metal objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands.

A classifier based approach for the detection of potential threats in CT based Baggage Screening

Recent years have seen increased use of Computed Tomography (CT) based Unaccompanied Baggage and Package Screening (UBPS) systems for luggage examination to ensure air travel security. In this paper we present a research work on developing a system for automatic detection of potential threat items in cluttered 3D CT imagery originating from UBPS systems by combining 3D medical image segmentation techniques with 3D shape classification and retrieval methods.

An evaluation of image denoising techniques applied to CT baggage screening imagery

This paper investigates the efficacy of several popular denoising methods in the previously unconsidered context of Computed Tomography (CT) baggage imagery. The performance of a dedicated CT baggage denoising approach (alpha-weighted mean separation and histogram equalisation) is compared to the following popular denoising techniques: anisotropic diffusion; total variation denoising; bilateral filtering; translation invariant wavelet shrinkage and non-local means filtering. In addition to a standard qualitative performance analysis (visual comparisons), denoising performance is evaluated with a recently developed 3D SIFT-based analysis technique that quantifies the impact of denoising on the implementation of automated 3D object recognition. The study yields encouraging results in both the qualitative and quantitative analyses, with wavelet thresholding producing the most satisfactory results. The results serve as a strong indication that simple denoising will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.

A novel intensity limiting approach to Metal Artefact Reduction in 3D CT baggage imagery

This paper introduces a novel technique for Metal Artefact Reduction (MAR) in the previously unconsidered context 3D CT baggage imagery. The output of a conventional sinogram completion-based MAR approach is refined by imposing an upper limit on the intensity of the corrected images and by performing post-filtering using the non-local means filter. Furthermore, performance is evaluated using a novel quantitative analysis technique, using the ratio of noisy 3D SIFT detection points identified, as well as a standard qualitative comparison (visual quality). The objective of the quantitative analysis is to evaluate the impact of MAR on the application of computer vision techniques for automatic object recognition. The study yields encouraging results in both the qualitative and quantitative analyses. The proposed method yields a significant improvement in performance when compared to algorithms based on linear interpolation and reprojection-reconstruction; especially in terms of reducing the occurrence of new artefacts in the corrected images. The results serve as a strong indication that MAR will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.

A 3D extension to cortex like mechanisms for 3D object class recognition

We introduce a novel 3D extension to the hierarchical visual cortex model used for prior work in 2D object recognition. Prior work on the use of the visual cortex standard model for the explicit task of object class recognition has solely concentrated on 2D imagery. In this paper we discuss the explicit 3D extension of each layer in this visual cortex model hierarchy for use in object recognition in 3D volumetric imagery. We apply this extended methodology to the automatic detection of a class of threat items in Computed Tomography (CT) security baggage imagery. The CT imagery suffers from poor resolution and a large number of artefacts generated through the presence of metallic objects. In our examination of recognition performance we make a comparison to a codebook approach derived from a 3D SIFT descriptor and demonstrate that the visual cortex method out-performs in this imagery. Recognition rates in excess of 95% with minimal false positive rates are demonstrated in the detection of a range of threat items.

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.

Fully automatic 3D Threat Image Projection: Application to densely cluttered 3D Computed Tomography baggage images

In this paper, we describe a Threat Image Projection (TIP) method designed for 3D Computed Tomography (CT) screening systems. The novel methodology automatically determines a valid 3D location in the passenger 3D CT baggage image into which a fictional threat 3D image can be inserted without violating the bag content. According to the scan orientation, the passenger bag content and the material of the inserted threat appropriate CT artefacts are generated using a Radon transform in order to make the insertion realistic. Densely cluttered 3D CT baggage images are used to validate our method. Experimental results confirm that our method is able to reliably insert threat items in challenging 3D images without providing any perceptible visual cue to human screeners.

Integration of Local Image Cues for Probabilistic 2D Pose Recovery

A novel probabilistic formulation for 2-D human pose recovery from monocular images is proposed. It relies on a bottom-up approach based on an iterative process between clustering and body model fitting. Body parts are segmented from the foreground by clustering a set of images cues. Clustering is driven by 2D human body model fitting to obtain optimal segmentation while the model is resized and its articulated configuration is updated according to the clustering result. This method neither requires a training stage, nor any prior knowledge of poses and appearance as characteristics of body parts are already embedded in the integrated cues. Furthermore, a probabilistic confidence measure is proposed to evaluate the expected accuracy of recovered poses. Experimental results demonstrate the accuracy and robustness of this new algorithm by estimating 2-D human poses from walking sequences.

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. 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, 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.