Internship Openings

We have a new internship opening at MERL:

MM721: Depth sensing

MERL is looking for a well qualified individual to work on novel technologies for depth sensing. The ideal candidate will have background on computational imaging and/or compressive sensing. Hands-on hardware prototyping and programming experience is a plus. The work will involve both theoretical and practical development and will lead to several publications. The duration of the internship is expected to be 3-6 months. Position is available immediately and through summer 2014. Candidates at or beyond the middle of a Ph.D. program are encouraged to apply. Please include position ID in the subject of the e-mail.

MERL is a great place for summer internships. Our interns have a great opportunity to be involved in cutting-edge research and interact with a number of their peers form around the world with a large variety of interests. We also have a great social program!

If you are looking for an internship in other areas, look at our openings page.

Publication List Update: Signal Representations and Embeddings

I finally managed to find some time to update my publications list with papers that appeared near the end of the summer. The first set of papers extends our work on signal representations and embeddings and reinforces the importance of embeddings in signal representation applications.

As I have mentioned before, embeddings have been proven very powerful for encoding signal distances, with many applications in signal-based retrieval. In [1, 2] we explore these embeddings further. Their most exciting property is their information scalability. That means that their complexity scales according to the complexity of information required in the application. Using higher dimensions and more bits, we can represent a signal with little distortion. Using fewer dimensions and fewer bits, we can represent only the distances of signals up to a radius, but not the signals themselves.

Continue reading Publication List Update: Signal Representations and Embeddings

[1]

S. Rane, P. T. Boufounos, and A. Vetro, “Quantized Embeddings: An Efficient and Universal Nearest Neighbor Method for Cloud-based Image Retrieval,” Proc. SPIE Applications of Digital Image Processing XXXVI, no. 8856, San Diego, CA, August 25-29, 2013.

[preprint] [Bibtex]

@inproceedings{RBV_SPIE13_Embeddings,
Address =   {San Diego, CA},
 Author =   {Rane, S. and Boufounos, P. T. and Vetro, A.},
 Booktitle =   {Proc. SPIE Applications of Digital Image Processing XXXVI},
 Number = {8856},
 Month =   {August 25-29},
 Title =   {Quantized Embeddings: An Efficient and Universal Nearest Neighbor Method for Cloud-based Image Retrieval},
 url = {https://doi.org/10.1117/12.2022286},
 doi = {10.1117/12.2022286},
 Pdf =     {http://boufounos.com/Publications/RBV_SPIE13_Embeddings.pdf},
 Year =   {2013}
}

[2]

P. T. Boufounos, “Angle-preserving Quantized Phase Embeddings,” Proc. SPIE Wavelets and Sparsity XV, no. 8858, San Diego, CA, August 25-29, 2013.

[preprint] [Bibtex]

@inproceedings{B_SPIE2013_APQPE,
Address =   {San Diego, CA},
 Author =   {Boufounos, P. T.},
 Booktitle =   {Proc. SPIE Wavelets and Sparsity XV},
 Month =   {August 25-29},
 Number = {8858},
 Title =   {Angle-preserving Quantized Phase Embeddings},
 Pdf =     {http://boufounos.com/Publications/B_SPIE2013_APQPE.pdf},
 Url =    {https://doi.org/10.1117/12.2024412},
 doi =    {10.1117/12.2024412},
 Year =   {2013}
}