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.