Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

Süleyman Uzun; Devrim Akgün

doi:10.18100/ijamec.266173

Konferans Bildirisi

Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

Yıl 2016, Special Issue (2016), 90 - 94, 01.12.2016

Süleyman Uzun Devrim Akgün

https://doi.org/10.18100/ijamec.266173

Öz

One of the widespread image processing applications is image filtering
with two dimensional convolution. Determining the weights of image filters are
of importance for the success of filtering operation. Heuristic algorithms such
as genetic algorithms provide an efficient way of training these types of
filters. Due to the high computational cost of repetitive image filtering
operations, this process may take hours to implement using single core
computing. OpenMP (Open Multi Processing) provides an efficient library for
utilizing the computing power of multicore processors. In this study, OpenMP accelerated training of
separable filters that are a subclass of convolution filters has been
implemented based on genetic algorithms. Comparative speed-up results for various
sizes of images using various sizes of filtering kernels were presented. Also
the effect of population size of genetic algorithm and the number of working
cores have been investigated.

Anahtar Kelimeler

OpenMP, separable filters, image processing

Kaynakça

R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). 2007.
[2] A. Karasaridis and E. Simoncelli, “A filter design technique for steerable pyramid image transforms,” 1996 IEEE Int. Conf. Acoust. Speech, Signal Process. Conf. Proc., vol. 4, pp. 2387–2390, 1996.
[3] J. Yang, L. Liu, T. Jiang, and Y. Fan, “A modified Gabor filter design method for fingerprint image enhancement,” Pattern Recognit. Lett., vol. 24, no. 12, pp. 1805–1817, 2003.
[4] R. Poli, “Genetic Programming for Image Analysis,” in Genetic Programming 1996: Proceedings of the First Annual Conference, 1996, pp. 363–368.
[5] D. Akgün and P. Erdoğmuş, “GPU accelerated training of image convolution filter weights using genetic algorithms,” Appl. Soft Comput., vol. 30, pp. 585–594, 2015.
[6] D. J. Krusienski and W. K. Jenkins, “Particle swarm optimization for adaptive IIR filter structures,” Evolutionary Computation, 2004. CEC2004. Congress on, vol. 1. p. 965–970 Vol.1, 2004.
[7] G. J. E. Rawlins, “Foundations of Genetic Algorithms,” in Foundations of Genetic Algorithms, 1991, vol. 21, p. 341.
[8] M. Haseyama and D. Matsuura, “A filter coefficient quantization method with genetic algorithm, including simulated annealing,” Signal Process. Lett. IEEE, 2006.
[9] D. M. Weber and D. P. Casasent, “Quadratic Gabor filters for object detection,” IEEE Trans. Image Process., vol. 10, no. 2, pp. 218–230, 2001.
[10] Y. Wang, B. Li, and Y. Chen, “Digital IIR filter design using multi-objective optimization evolutionary algorithm,” Appl. Soft Comput., 2011.

Yıl 2016, Special Issue (2016), 90 - 94, 01.12.2016

Süleyman Uzun Devrim Akgün

https://doi.org/10.18100/ijamec.266173

Öz

Kaynakça

R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). 2007.
[2] A. Karasaridis and E. Simoncelli, “A filter design technique for steerable pyramid image transforms,” 1996 IEEE Int. Conf. Acoust. Speech, Signal Process. Conf. Proc., vol. 4, pp. 2387–2390, 1996.
[3] J. Yang, L. Liu, T. Jiang, and Y. Fan, “A modified Gabor filter design method for fingerprint image enhancement,” Pattern Recognit. Lett., vol. 24, no. 12, pp. 1805–1817, 2003.
[4] R. Poli, “Genetic Programming for Image Analysis,” in Genetic Programming 1996: Proceedings of the First Annual Conference, 1996, pp. 363–368.
[5] D. Akgün and P. Erdoğmuş, “GPU accelerated training of image convolution filter weights using genetic algorithms,” Appl. Soft Comput., vol. 30, pp. 585–594, 2015.
[6] D. J. Krusienski and W. K. Jenkins, “Particle swarm optimization for adaptive IIR filter structures,” Evolutionary Computation, 2004. CEC2004. Congress on, vol. 1. p. 965–970 Vol.1, 2004.
[7] G. J. E. Rawlins, “Foundations of Genetic Algorithms,” in Foundations of Genetic Algorithms, 1991, vol. 21, p. 341.
[8] M. Haseyama and D. Matsuura, “A filter coefficient quantization method with genetic algorithm, including simulated annealing,” Signal Process. Lett. IEEE, 2006.
[9] D. M. Weber and D. P. Casasent, “Quadratic Gabor filters for object detection,” IEEE Trans. Image Process., vol. 10, no. 2, pp. 218–230, 2001.
[10] Y. Wang, B. Li, and Y. Chen, “Digital IIR filter design using multi-objective optimization evolutionary algorithm,” Appl. Soft Comput., 2011.

Toplam 10 adet kaynakça vardır.

Ayrıntılar

Konular	Mühendislik
Bölüm	Research Article
Yazarlar	Süleyman Uzun Devrim Akgün
Yayımlanma Tarihi	1 Aralık 2016
Yayımlandığı Sayı	Yıl 2016 Special Issue (2016)

Kaynak Göster

APA	Uzun, S., & Akgün, D. (2016). Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter. International Journal of Applied Mathematics Electronics and Computers(Special Issue-1), 90-94. https://doi.org/10.18100/ijamec.266173
AMA	Uzun S, Akgün D. Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter. International Journal of Applied Mathematics Electronics and Computers. Aralık 2016;(Special Issue-1):90-94. doi:10.18100/ijamec.266173
Chicago	Uzun, Süleyman, ve Devrim Akgün. “Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1 (Aralık 2016): 90-94. https://doi.org/10.18100/ijamec.266173.
EndNote	Uzun S, Akgün D (01 Aralık 2016) Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 90–94.
IEEE	S. Uzun ve D. Akgün, “Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter”, International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, ss. 90–94, Aralık 2016, doi: 10.18100/ijamec.266173.
ISNAD	Uzun, Süleyman - Akgün, Devrim. “Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter”. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (Aralık 2016), 90-94. https://doi.org/10.18100/ijamec.266173.
JAMA	Uzun S, Akgün D. Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter. International Journal of Applied Mathematics Electronics and Computers. 2016;:90–94.
MLA	Uzun, Süleyman ve Devrim Akgün. “Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, 2016, ss. 90-94, doi:10.18100/ijamec.266173.
Vancouver	Uzun S, Akgün D. Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter. International Journal of Applied Mathematics Electronics and Computers. 2016(Special Issue-1):90-4.