Sampling at unknown locations: uniqueness and reconstruction under constraints

Co-authors

Golnoosh Elhami, Michalina Pacholska, Benjamín Béjar Haro and Martin Vetterli.


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Abstract

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is often hopeless. For example, we recently showed that, for polynomial

Shape from bandwidth: The 2-D orthogonal projection case

Co-authors

Benjamín Bejar Haro and Martin Vetterli.


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Full text: View at publisher, Infoscience.
Cite: Bibtex.
Code: Run in browser (using binder), Infoscience.


Abstract

Could bandwidth – one of the most classic concepts in signal processing – have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our

Unlabeled sensing: Reconstruction algorithm and theoretical guarantees

Co-authors

Golnoosh Elhami, Benjamín Bejar Haro and Martin Vetterli.


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Full text: View at publisher, Infoscience.
Cite: Bibtex.


Abstract

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of each sample is known and that the only distortion present in the observations is due to additive measurement noise. However, there are some applications

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