I have been to these Non Conventional Optical Imaging meetings at least three times and every time enjoyed them very much. This time, I decided to send a proposal in for a presentation at the next meeting. We'll see how that goes. If you are interested in that talk, let me know (and yes I think I lied about the 15 minutes but it's just between the two of us)
From Direct Imaging to Machine Learning … and more, in 15 minutesDe l'imagerie directe au Machine Learning … et plus, en 15 minutesIgor CarronWe will present a panorama of sensing techniques from direct imaging all the way to Machine Learning. In particular, we will show that the traditional barriers between these fields are becoming porous and that in order to conceive new sensors, the use of the structure of the objects being observed as an a priori, is becoming central. Traditionally, this task used to be the domain of signal processing experts at the end of the data acquisition chain. For the past ten years, the use of a priori knowledge, such as sparsity, low-rankedness or more generally the manifold in which the signal “lives”, has enabled the development of new mathematical approaches and attendant numerical solution techniques [3,4]. In fact, the appearance of these tools has provided the applied and sometimes the not-so-applied mathematicians or the signal processing experts a direct say in the conception of new detectors. We will see in particular how the appearance of sharp phase transitions linked to how much information can be transmitted by the sensor, now provides new rules on the conception and calibration of conventional and non-conventional imaging sensors. This expository talk will use, in part, material from [1,2] and examples featured in [5].Nous présenterons un panorama des différentes techniques qui permettent de capter l'information partant du capteur conventionnel en imagerie directe jusqu'aux techniques de Machine Learning utilisées dans l'apprentissage de données de très grandes dimensions. Nous verrons que les barrières deviennent de plus en plus poreuses entre ces domaines et que pour la conception de nouveaux capteurs, l'imagerie doit pouvoir utiliser la structure de l'objet étudié. Traditionnellement, cette connaissance a priori de l'objet observé a souvent été réservée au traitement du signal en fin de la chaine d'acquisition. Cette connaissance à priori de la structure du signal, parcimonie, rang faible ou l’appartenance a une sous variété, a permis depuis 10 ans (au mois près) un développement très rapide de nouveaux outils mathématiques et numériques [3,4]. L'irruption de ces outils permet maintenant au mathématicien et à l'ingénieur du traitement du signal d'être les initiateurs ou des parties prenantes de la conception de nouveaux détecteurs. Nous verrons en particulier comment l'apparition de transitions de phase abruptes, liées à l'information transmise en imagerie, donnent des règles claires sur la conception et la calibration de nouveaux imageurs conventionnels et non conventionnels. Cet exposé reprendra en partie des éléments et références mentionnés dans [1,2] et des exemples pris d'articles mentionnés dans [5].Références :[1] Sunday Morning Insight: A Quick Panorama of Sensing from Direct Imaging to Machine Learning, http://nuit-blanche.blogspot.com/2013/06/sunday-morning-sunday-quick-panorama-of.html[2] Sunday Morning Insight: The Map Makers, http://nuit-blanche.blogspot.com/2013/11/sunday-morning-insight-map-makers.html[3] Advanced Matrix Factorization Jungle Page, https://sites.google.com/site/igorcarron2/matrixfactorizations[4] The Big Picture in Compressive Sensing, https://sites.google.com/site/igorcarron2/cs
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