François Chung, Ph.D.

Tag: supervised learning

Neural networks and deep learning

Neural networks and deep learning

Coursera training, MOOC (2018). Given online by Stanford University (US), this training introduces the foundations of deep learning. Main objectives are to understand the major technology trends driving deep learning, be able to build, train, apply and implement fully connected deep neural networks, and understand their key parameters. The training aims to teach how deep […]

EUE 2017 - Book

EUE 2017 – Book

Publication François Chung, Tomás Rodríguez; Multi-focal Image Segmentation, Classification and Authentication: A General Framework applied on Microscope Pollen Images; Éditions universitaires européennes (EUE), Saarbrücken, 2017; ISBN: 978-3841677907. Abstract In this book, we propose a general framework for multi-focal image segmentation, classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be […]

Inspiralia 2013 - Technical report

Inspiralia 2013 – Technical report

Publication François Chung, Tomás Rodríguez; A general framework for multi-focal image classification and authentication: Application to microscope pollen images; Inspiralia, Madrid, 2013. Abstract In this article, we propose a general framework for multi-focal image classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on […]

TVC 2011 - Academic journal

TVC 2011 – Academic journal article

Publication François Chung, Jérôme Schmid, Nadia Magnenat-Thalmann, Hervé Delingette; Comparison of statistical models performance in case of segmentation using a small amount of training datasets; In: The Visual Computer (TVC), 27 (2), pp. 141-151, 2011. Abstract Model-based image segmentation has been extensively used in medical imaging to learn both shape and appearance of anatomical structures from […]