3D segmentation of the tracheobronchial tree using multiscale morphology enhancement filter

In this article we present a new region growing algorithm for airway segmentation based on multiscale black tophat enhancement filter. Lung airways are tubular structures that display specific characteristics, such as highly variable intensity levels within the lumen and proximity to vessels. The proposed airways enhancement filter aims to separate airways from adjacent lung parenchyma and vessel. Based on the filter ouput, the region growing is performed in order to delineate the airways and then to reconstruct the tracheobronchial tree. The proposed method has been applied on various CT scans. In this paper, an experimental comparison study between our filter and the "gold standard" filters used to enhance tubular structures (Frangi, Sato and Krissian filters) followed by a region growing process is performed on data from the VESSEL12 challenge framework. Our approach outperforms the other considered methods in terms of retrieved bronchi and computing time

FACE, GENDER AND RACE CLASSIFICATION USING MULTI-REGULARIZED FEATURES LEARNING

This paper investigates a new approach for face, gender and race classification, called multi-regularized learning (MRL). This approach combines ideas from the recently proposed algorithms called multi-stage learning (MSL) and multi-task features learning (MTFL). In our approach, we first reduce the dimensionality of the training faces using PCA. Next, for a given a test (probe) face, we use MRL to exploit the relationships among multiple shared stages generated by changing the regularization parameter. Our approach results in convex optimization problem that controls the trade-off between the fidelity to the data (training) and the smoothness of the solution (probe). Our MRL algorithm is compared against different state-of-the-art methods on face recognition (FR), gender classification (GC) and race classification (RC) based on different experimental protocols with AR, LFW, FEI, Lab2 and Indian databases. Results show that our algorithm performs very competitively

Could Multimedia approaches help Remote Sensing Analysis?

International audienceThe paper explores how multimedia approaches used in image understanding tasks could be adapted and used in remote sensing image analysis. Two approaches are investigated: the classical Bag of Visual Words (BoVW) approach and the Deep Learning approach. Tests are performed for the classification of the UC Merced Land Use Dataset which provide better results than the state of the art

Compression de maillages 3D de grande résolution par transformée en ondelettes au « fil de l'eau »

Dans ce papier, nous nous intéressons à la compression dans le domaine des ondelettes de très grands maillages au fur et à mesure de leur acquisition. Nous proposons une méthode de compression progressive utilisant une transformée en ondelettes au "fil de l'eau" pour les maillages 3D semi-réguliers. Cette méthode consiste à faire le traitement au fur et à mesure de l'acquisition des données tout en réduisant considérablement l'espace mémoire utilisé. Les résultats expérimentaux montrent que la méthode est très efficace en terme de coût calculatoire, espace et accès mémoire. Les rapports débit/distorsion de notre méthode au fil de l'eau sont très proches de ceux de la méthode avec chargement de l'objet en totalité avant traitement, mais présente l'avantage de nécessiter un espace mémoire beaucoup plus réduit