Seminar/2018-06-13
From LRDE
Mercredi 13 juin 2018, 11h-12h, Amphi 401
Hierarchical image representations: construction, evaluation and examples of use for image analysis
Camille Kurtz (LIPADE, Université Paris Descartes)
Hierarchical image representations have become increasingly popular in
image processing and computer vision over the past decades. Indeed,
they allow a modeling of image contents at different (and
complementary) levels of scales, resolutions and semantics. Methods
based on such image representations have been able to tackle various
complex challenges such as multi-scale image segmentation, image
filtering, object detection, recognition, and more recently image
characterization and understanding. In this talk, we will focus on the
binary partition tree (BPT), which is a well-known hierarchical
data-structure, frequently involved in the design of image
segmentation strategies. In a first part, we will focus on the
construction of such trees by providing a generalization of the BPT
construction framework to allow one to embed multiple features, which
enables handling many metrics and/or many images. In a second part,
we will discuss how it may be possible to evaluate the quality of such
a structure and its ability to reconstruct regions of the image
corresponding to segments of reference given by a user. Finally, we
will see some examples of image analysis and recognition processes
involving these hierarchical structures. The main thematic application
is remote sensing and satellite image analysis.
Camille Kurtz obtained the MSc and PhD from Université de Strasbourg,
France, in 2009 and 2012. He was a post-doctoral fellow at Stanford
University, CA, USA, between 2012 and 2013. He is now an Associate
Professor at Université Paris Descartes, France. His scientific
interests include image analysis, data mining, medical imaging and
remote sensing.
www.math-info.univ-paris5.fr/~ckurtz/