Room 805
Communications and Signal Processing Group
EEE Department
Imperial College London
Exhibition Road
London SW7 2AZ, United Kingdom

jon.onativia@imperial.ac.uk

View Jon OƱativia's profile on LinkedIn

About me

I am currently a Research Assistant and PhD candidate in the Communications and Signal Processing Group at Imperial College London. My supervisor is Dr Pier Luigi Dragotti.


Short bio

I started my undergrad studies in the University of the Basque Country in 2002 where I completed the first 3 years out of 5 of the Ingenierio de Telecomunicación degree. The first year I received the Special Prize for best Academic Records with a GPA of 8.14/10 (best GPA out of 120 students). In 2005 I transferred to the Universidad Politécnica de Madrid (UPM), where I enrolled in a double degree program with Télécom ParisTech. In 2008 I obtained both degrees, Ingenierio de Telecomunicación from the UPM and Ingénieur diplômé de l'École Nationale Supérieure des Télécommunictions (Promo 2008).

During my studies I did two internships, one at Vicomtech where I worked in augmented reality applications for digital television and another at General Electric Healthcare where I collaborated in the development of a prototype for 3D mammography review with image processing features. I then worked for two years and a half at NDS (part of Cisco) where I participated in a variety of projects related to digital televsion (multimedia home networking and DRM protected content distribution over IP, gestural recognition UI design using depth measurement cameras, etc).

In 2010 I came to Imperial College London where I obtained an MSc in Communications and Signal Processing with Distinction. In November 2011 I started my PhD under the supervision of Dr Pier Luigi Dragotti.


Research

My current research is in the field of modern sampling theory (sampling finite rate of innovation signals, sparsity and uncertainty principles, etc). I am currently working on extending the theory to new type of signals and applying it to neural signals for spike inference. I have also worked in compression of multiview images.

Journal papers


Conference papers

  • Jon Oñativia, Yue Lu and Pier Luigi Dragotti, Finite dimensional FRI, accepted for publication, ICASSP 2014.
  • Pier Luigi Dragotti, Jon Oñativia, Jose Antonio Urigüen and Thierry Blu, Approximate Strang-Fix: Sampling infinite streams of Diracs with any kernel, Proc. SPIE 8858, Wavelets and Sparsity XV, August, 2013. ([PDF])
  • Jon Oñativia, Jose Antonio Urigüen and Pier Luigi Dragotti, Sequential Local FRI Sampling of Infinite Streams of Diracs, ICASSP 2013, May 2013. ([PDF] [slides])
  • A. Gelman, J. Oñativia and P.L. Dragotti, A Fast Layer-based Multiview Image Coding Algorithm, EUSIPCO 2012, August 2012. ([PDF])
  • S. Bernard, S. Muller and J. Onativia, Computer-Aided Microcalcification Detection on Digital Breast Tomosynthesis Data: A Preliminary Evaluation, IWDM 2008, pp. 151-157, July 2008.

Conference abstracts

  • S.R. Schultz, J. Oñativia, J.A. Urigüen and P.L. Dragotti, A Finite Rate of Innovation Algorithm for Spike Detection from Two-Photon Calcium Imaging, Neuroscience 2012, October 2012.

FRI spike train detection from two-photon calcium imaging

MATLAB implementation for spike train detection from two-photon calcium imaging applying FRI techniques. If you use this software, we request that you cite:

The zip file contains a number of files to reproduce the results presented in the paper:

  • File real_data.m runs the double consistency algorithm on real data and reproduces figure 7 of the journal paper.
  • File surrogate_data.m runs the double consistency algorithm on surrogate data.
  • File fig3.m reproduces figure 3 of the journal paper.
  • File fig9.m reproduces figure 9 of the journal paper.


Layer-based multiview image compression

MATLAB toolkit to compress multiview images. Contains compiled functions (MEX files) for Windows 32 bits platform. Requires Cygwin.

If you use this software, we request that you cite:

  • A. Gelman, P.L. Dragotti and V. Velisavljevic, Multiview Image Coding using Depth Layers and an Optimized Bit Allocation, IEEE Transactions on Image Processing, vol. 21, no. 9, pp 4092-4105, Sept. 2012.
  • A. Gelman, J. Oñativia and P.L. Dragotti, A Fast Layer-based Multiview Image Coding Algorithm, EUSIPCO 2012, August 2012.
The zip file contains datasets from the "Middlebury Stereo Datasets". However, the "Animal Farm" dataset is original and if you use it, please quote the following paper:
  • A. Gelman, J. Berent and P.L. Dragotti, Layer-based Sparse Representation of Multiview Images, EURASIP Journal on Advances in Signal Processing, Mar. 2012