Gaze guidance and efficient visual representations

I will present an overview of our research on gaze prediction and gaze guidance (see For prediction we use efficient visual representations (derived from differential geometry) to model visual saliency, and support-vector machines (SVNs) to obtain the predictions. An alternative approach to efficient representation is based on the principle of sparse coding. To obtain the local transformations needed for gaze guidance we use low-dimensional visual representations (signal energy on different spatio-temporal scales) and SVNs.



PD Dr.-Ing. Erhardt Barth
Institut für Neuro- und Bioinformatik
Universität Lübeck


Mittwoch, 03. Februar 2010, 16 Uhr


Universität Ulm, Oberer Eselsberg, N27, Raum 2.033