Ponencia
On the design of a sparsifying dictionary for compressive image feature extraction
Autor/es | Trevisi, Marco
Carmona Galán, Ricardo Fernández Berni, Jorge Rodríguez Vázquez, Ángel Benito |
Departamento | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo |
Fecha de publicación | 2015 |
Fecha de depósito | 2019-10-11 |
Publicado en |
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ISBN/ISSN | 978-1-5090-0246-7 |
Resumen | Compressive sensing is an alternative to Nyquist-rate sampling when the signal to be acquired is known to be sparse or compressible. A sparse signal has a small number of nonzero components compared to its total length. ... Compressive sensing is an alternative to Nyquist-rate sampling when the signal to be acquired is known to be sparse or compressible. A sparse signal has a small number of nonzero components compared to its total length. This property can either exist either in the sampling domain, i. e. time or space, or with respect to a transform basis. There is a parallel between representing a signal in a compressed domain and feature extraction. In both cases, there is an effort to reduce the amount of resources required to describe a large set of data. A given feature is often represented by a set of parameters, which only acquire a relevant value in a few points in the image plane. Although there are some works reported on feature extraction from compressed samples, none of them considers the implementation of the feature extractor as a part of the sensor itself. Our approach is to introduce a sparsifying dictionary, feasibly implementable at the focal plane, which describes the image in terms of features. This allows a standard reconstruction algorithm to directly recover the interesting image features, discarding the irrelevant information. In order to validate the approach, we have integrated a Harris-Stephens corner detector into the compressive sampling process. We have evaluated the accuracy of the reconstructed corners compared to applying the detector to a reconstructed image. |
Identificador del proyecto | TEC2012-38921-C02
IPT-2011-1625-430000 IPC-20111009 TIC 2338-2013 N000141410355 |
Cita | Trevisi, M., Carmona Galán, R., Fernández Berni, J. y Rodríguez Vázquez, Á.B. (2015). On the design of a sparsifying dictionary for compressive image feature extraction. En 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (689-692), El Cairo, Egipto: Institute of Electrical and Electronics Engineers. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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On the Design of a Sparsifying.pdf | 287.8Kb | [PDF] | Ver/ | |