Chers collègues, La prochaine session du séminaire Performance et Généricité du LRDE (Laboratoire de Recherche et Développement de l'EPITA) aura lieu le Mardi 17 décembre 2019 (10h -- 11h), IP12A. Vous trouverez sur le site du séminaire [1] les prochaines séances, les résumés, captations vidéos et planches des exposés précédents [2], le détail de cette séance [3] ainsi que le plan d'accès [4]. [1] http://seminaire.lrde.epita.fr <http://seminaire.lrde.epita.fr/> [2] http://seminaire.lrde.epita.fr/Archives <http://seminaire.lrde.epita.fr/Archives> [3] http://seminaire.lrde.epita.fr/2019-12-17 <http://seminaire.lrde.epita.fr/2019-12-17> [4] http://www.lrde.epita.fr/wiki/Contact <http://www.lrde.epita.fr/wiki/Contact> Au programme du Mardi 17 décembre 2019 : * 10h -- 11h: Learning the relationship between neighboring pixels for some vision tasks -- Yongchao Xu, Associate Professor at the School of Electronic Information and Communications, HUST, China http://www.vlrlab.net/~yxu/ <http://www.vlrlab.net/~yxu/> The relationship between neighboring pixels plays an important role in many vision applications. A typical example of a relationship between neighboring pixels is the intensity order, which gives rise to some morphological tree-based image representations (e.g., Min/Max tree and tree of shapes). These trees have been shown useful for many applications, ranging from image filtering to object detection and segmentation. Yet, these intensity order based trees do not always perform well for analyzing complex natural images. The success of deep learning in many vision tasks motivates us to resort to convolutional neural networks (CNNs) for learning such a relationship instead of relying on the simple intensity order. As a starting point, we propose the flux or direction field representation that encodes the relationship between neighboring pixels. We then leverage CNNs to learn such a representation and develop some customized post-processings for several vision tasks, such as symmetry detection, scene text detection, generic image segmentation, and crowd counting by localization. This talk is based on [1] and [2], as well as extension of those previous works that are currently under review. [1] Xu, Y., Wang, Y., Zhou, W., Wang, Y., Yang, Z. and Bai, X., 2019. Textfield: Learning a deep direction field for irregular scene text detection. IEEE Transactions on Image Processing. [2] Wang, Y., Xu, Y., Tsogkas, S., Bai, X., Dickinson, S. and Siddiqi, K., 2019. DeepFlux for Skeletons in the Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. -- Yongchao Xu received in 2010 both the engineer degree in electronics & embedded systems at Polytech Paris Sud and the master degree in signal processing & image processing at Université Paris Sud, and the Ph.D. degree in image processing and mathematical morphology at Université Paris Est in 2013. After completing his Ph.D. study at LRDE, EPITA, ESIEE Paris, and LIGM, He worked at LRDE as an assistant professor (Maître de Conférences). He is currently an Associate Professor at the School of Electronic Information and Communications, HUST. His research interests include mathematical morphology, image segmentation, medical image analysis, and deep learning. L'entrée du séminaire est libre. Merci de bien vouloir diffuser cette information le plus largement possible. N'hésitez pas à nous faire parvenir vos suggestions d'orateurs. -------------- section suivante -------------- Une pièce jointe HTML a été nettoyée... URL: <http://lists.lrde.epita.fr/pipermail/seminaire/attachments/20191211/c06b5da5/attachment.html>