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Proceedings Paper • Open Access

Machine Learning, deep learning and optimization in computer vision
Author(s): Stéphane Canu

Paper Abstract

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

Paper Details

Date Published: 14 May 2017
PDF: 1 pages
Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380N (14 May 2017); doi: 10.1117/12.2277468
Show Author Affiliations
Stéphane Canu, Institut National des Sciences Appliquées de Rouen (France)
Normandie Univ. (France)

Published in SPIE Proceedings Vol. 10338:
Thirteenth International Conference on Quality Control by Artificial Vision 2017
Hajime Nagahara; Kazunori Umeda; Atsushi Yamashita, Editor(s)

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