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.
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