Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

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.

Keywords:

OpenMP separable filters, image processing,

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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.

International Journal of Applied Mathematics Electronics and Computers-Cover

ISSN: 2147-8228
Yayın Aralığı: Yılda 4 Sayı
Başlangıç: 2013
Yayıncı: Selçuk Üniversitesi

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