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

Association de données par la théorie des fonctions de croyance (application au suivi audio et vidéo des individus)

Ce mémoire présente une méthode d'association crédibiliste de données dans le contexte du suivi multi-objet par un système de perception multi-capteur hétérogènes. La méthode d'association proposée est formulée dans le cadre du monde ouvert considéré comme non exhaustif et dans le contexte des modèles de Fonctions de Croyance Transférables. La détection des phénomènes d'apparition et de disparition des objets est réalisée par une analyse spécifique de la masse conflictuelle résultante de la phase de combinaison des informations. Afin de valider la méthode d'association proposée, nous nous sommes placés dans le cadre d'une application de suivi d'individus par un système de perception bimodal audio et vidéo. L'étape de validation est réalisée sur la combinaison de données réelles et de données synthétisées. Les résultats de ces simulations sont très encourageants et témoignent de l'efficacité de notre nouvelle méthode d'association.LILLE1-BU (590092102) / SudocSudocFranceF

Integration of local image cues for Probabilistic 2D pose recovery

Abstract. 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.

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. Index Terms — Image denoising, baggage CT, 3D SIFT 1