Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images.

Franchini, S., Gentile, A., Vassallo, G., Vitabile, S., Sorbello, F. (2013). A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra. In Proceedings of Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2013) (pp.128-135). IEEE Press [10.1109/CISIS.2013.29].

A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra

FRANCHINI, Silvia Giuseppina;GENTILE, Antonio;VASSALLO, Giorgio;VITABILE, Salvatore;SORBELLO, Filippo
2013-01-01

Abstract

Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
4-lug-2013
Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2013)
Taichung, Taiwan
3-5 luglio 2013
Seventh
2013
8
Franchini, S., Gentile, A., Vassallo, G., Vitabile, S., Sorbello, F. (2013). A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra. In Proceedings of Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2013) (pp.128-135). IEEE Press [10.1109/CISIS.2013.29].
Proceedings (atti dei congressi)
Franchini, S; Gentile, A; Vassallo, G; Vitabile, S; Sorbello, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/90190
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