L'équipe A3SI du LIGM (unité mixte de recherche de l'Université Paris Est) organise un atelier doctorants le jeudi 19 mai 2016 de 13h00 à 15h00 à ESIEE Paris (amphi 260).
Quatre doctorants exposeront leurs travaux :
Connected morphological operators based on hierarchical image models have been increasingly considered to provide efficient image segmentation and filtering tools in various application fields, e.g. (bio)medical imaging, astronomy or satellite imaging. Among hierarchical image models, component-trees represent the structure of grey-level images by considering their nested binary level-sets obtained from successive thresholds. Recently, a new notion of component-graph was introduced to extend the component-tree to model any greylevel or multivalued images. The notion of shaping was also recently introduced as a way to improve the antiextensive filtering of grey-level images by considering a two-layer component-tree for grey-level image processing. In this article, we study how component-graphs (that extend the component-tree from a spectral point of view) and shapings (that extends the component-tree from a conceptual point of view) can be associated for the effective processing of multivalued images. The relevance and usefulness of such association are illustrated by applicative examples.
Usual SfM techniques based on feature points have a hard time on scenes with little texture or presenting a single plane, as in indoor environments. Line segments are more robust features in this case. We propose a novel geometrical criterion for two-view pose estimation using lines, that does not assume a Manhattan world. We also define a parameterless (a contrario) RANSAC-like method to discard calibration outliers and provide more robust pose estimations, possibly using points as well when available. Finally, we provide quantitative experimental data that illustrate failure cases of other methods and that show how our approach outperforms them, both in robustness and precision.
Retinal recognition is an attractive topic of scientific research, due to its unicity, universality and robustness. However, existing systems may suffer from some issues. Indeed, due to the retinography acquisition process, retinal images are often affected by imperfections such as poor gray level contrast, noise and background intensity variation. In the other hand, the dense structure of vessels in retina increases the execution time of key point based recognition process and the rate of mismatching individuals. In order to overcome these problems, we propose in this work a new method called "ODR" (Optical Disc interest Ring) which improves the retinal image quality and extracts an interest ring around the detected optical disc. For evaluation, ODR based both identification and verification systems are assessed through SIFT and SURF description and a subset of the VARIA healthy retinal database. Obtained results show the efficiency of our proposed method which allows speeding up the different evaluated systems while ensuring a high accuracy rate (more than 99%) and outperforming existing systems. In addition, more experiments are conducted on medical STARE and DRIVE databases. Promising results are also obtained.
Euclidean rotations in R^n are bijective and isometric maps. Nevertheless, they lose these properties when digitized in Z^n. For n=2, the subset of bijective digitized rotations has been described explicitly by Nouvel and Rémila and more recently by Roussillon and Coeurjolly. In the case of 3D digitized rotations, the same characterization has remained an open problem. We propose an algorithm for certifying the bijectivity of 3D digitized rational rotations using the arithmetic properties of the Lipschitz quaternions.