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 Ingeniero 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,
Ingeniero de Telecomunicación from the UPM and Ingénieur diplômé de l'École
Nationale Supérieure des Télécommunications (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 M. Lu and Pier Luigi Dragotti,
Sparsity pattern recovery using FRI methods, IEEE ICASSP 2015
(April 2015) [PDF].
- Jon Oñativia, Yue Lu and Pier Luigi Dragotti,
Finite dimensional FRI, IEEE ICASSP 2014 (May 2014)
[PDF].
- 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,
IEEE 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
- Jon Oñativia, Pier Luigi Dragotti and Yue M. Lu
Sparsity According to Prony, average performance analysis,
SPARS Workshop 2015, Cambridge, UK (July 2015)
[PDF].
- Jon Oñativia and Pier Luigi Dragotti,
Sparsity According to Prony: From Structured to Unstructured
Representations and Back,
International BASP Frontiers workshop 2015, Villars-sur-Ollon, Switzerland (January 2015).
- Jon Oñativia and Pier Luigi Dragotti,
Finite dimensional FRI for reconstruction of sparse signals,
UCL-Duke workshop on Sensing and Analysis of High-Dimensional Data, London, UK (September 2014).
- 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,
SfN - Neuroscience 2012, New Orleans, USA (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