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- Evaggelia Tsiligianni, Lisimachos P. Kondi, Aggelos K. Katsaggelos
- IEEE Transactions on Information Theory
- 2014

Despite the important properties of unit norm tight frames (UNTFs) and equiangular tight frames (ETFs), their construction has been proven extremely difficult. The few known techniques produce only a small number of such frames while imposing certain restrictions on frame dimensions. Motivated by the application of incoherent tight frames in compressed… (More)

- Evaggelia Tsiligianni, Lisimachos P. Kondi, Aggelos K. Katsaggelos
- 2012 Proceedings of the 20th European Signal…
- 2012

Compressed sensing (CS) theory relies on sparse representations in order to recover signals from an undersampled set of measurements. The sensing mechanism is described by the projection matrix, which should possess certain properties to guarantee high quality signal recovery, using efficient algorithms. Although the major breakthrough in compressed sensing… (More)

- Evaggelia Tsiligianni, Lisimachos P. Kondi, Aggelos K. Katsaggelos
- IEEE Transactions on Image Processing
- 2010

In object-based video representation, video scenes are composed of several arbitrarily shaped video objects (VOs), defined by their texture, shape and motion. In error-prone communications, packet loss results in missing information at the decoder. The impact of transmission errors is minimized through error concealment. In this paper, we propose a spatial… (More)

- Evaggelia Tsiligianni, Lisimachos P. Kondi, Aggelos K. Katsaggelos
- IEEE Signal Processing Letters
- 2015

Performance guarantees for the algorithms deployed to solve underdetermined linear systems with sparse solutions are based on the assumption that the involved system matrix has the form of an incoherent unit norm tight frame. Learned dictionaries, which are popular in sparse representations, often do not meet the necessary conditions for signal recovery. In… (More)

Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guarantees with high probability; however,… (More)

Performance guarantees for recovery algorithms employed in sparse representations, and compressed sensing highlights the importance of incoherence. Optimal bounds of incoherence are attained by equiangular unit norm tight frames (ETFs). Although ETFs are important in many applications, they do not exist for all dimensions, while their construction has been… (More)

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