SYNERGISTIC FIBROBLAST OPTIMIZATION BASED BOUNDARY DETECTION IN TAMIL SIGN LANGUAGE IMAGES

Authors

  • M Krishnaveni Assistant Professor Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
  • P Subashini Professor Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
  • T T Dhivyaprabha Research Scholar, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

DOI:

https://doi.org/10.20319/mijst.2017.32.193208

Keywords:

Tamil Sign Language, Synergistic Fibroblast Optimization, Canny Edge Technique, Edge Detection, Thresholding, Similarity Index

Abstract

Sign Language (SL) is a three dimensional language used for communication by deaf people. The recognition system for SL is always an apprehensive task which is handled by vision collaboration and technology. Basically, detection of edges is deliberated to be the precursor for detection of objects, as the edges are the outline of the objects. Detecting continuous edges in real time images is a hard problem, especially in Tamil Sign Language (TSL) recognition system. This paper proposes an algorithm which finds optimal threshold values (L and H) based on Synergistic Fibroblast Optimization (SFO) for detection of continuous, smooth and thin edges of TSL hand pose images. A novel SFO algorithm is proposed with sphere objective function and two constraints for reducing the ruined edges. The efficiency of the algorithm is compared experimentally with conventional Canny, Classical PSO and variant based PSO on TSL Consonants images. Experimental results suggested that the novel SFO based canny operator detects edges more accurately, and the edges detected are smoother and thinner when compared to other analyzed algorithms.

References

Avneetkaur, Lakhwinderkaur, & Savitha Gupta. (2012). Image recognition using coefficient of correlation and structural similarity index in uncontrolled environment. International Journal of Computer Applications, 59 (5), 32-39.

Canny, J. (1986). A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence, 8(6), 679-698. https://doi.org/10.1109/TPAMI.1986.4767851

Chinni. Jayachandra, Venkateswara Reddy, H.(2013). Iris recognition based on pupil using canny edge detection and k-means algorithm. International Journal Of Engineering And Computer Science, 2(1),221-225.

Ghate, P. (1990). An introduction to the signing system for Indian languages. Part II - Additional Signs. Bombay: Ali Yavar Jung National Institute for the Hearing Handicapped.

Hart, P. E. (2009). How the Hough transform was invented. IEEE Signal Processing Magazine, 26(6), 18–22. https://doi.org/10.1109/MSP.2009.934181

Hui Pan, Liang Wang, & Bo Liu. (2006). Particle swarm optimization for function optimization in noisy environment. Journal of Applied Mathematics and Computation, 181(2), 908–919. https://doi.org/10.1016/j.amc.2006.01.066

Jansi, S., & Subashini, P. (2012). Optimized adaptive thresholding based edge detection method for MRI brain images. International Journal of Computer Applications, 51(20), 1-8. https://doi.org/10.5120/8155-1525

John Dallon, C., & Jonathan Sherratt, A. (1998). A mathematical model for fibroblast and collagen orientation. Bulletin of Mathematical Biology. 60, 101-129. https://doi.org/10.1006/bulm.1997.0027

Krishnaveni, M., Subashini, P., & Dhivyaprabha, T.T. (2016). Improved canny edges using cellular based particle swarm optimization technique for tamil sign digital images. International Journal of Electrical and Computer Engineering, 6(5), 2158-2166. https://doi.org/10.11591/ijece.v6i5.11222

Mamta Junej , Parvinder Singh Sandhu. (2009). Performance evaluation of edge detection techniques for images in spatial domain. International Journal of Computer Theory and Engineering, 1(5),1793-8201. https://doi.org/10.7763/IJCTE.2009.V1.100

Masoud Nosrati, Ronak Karimi, Mehdi Hariri, Kamran Malekian. (2013). Edge detection techniques in processing digital images: Investigation of canny algorithm and gabor method. World Applied Programming, 3(3), 116-121.

Nisha, Rajesh Mehra, Lalita Sharma. (2015). Comparative analysis of canny and prewitt edge detection techniques used in image processing. International Journal of Engineering Trends and Technology, 28(1), 48-53. https://doi.org/10.14445/22315381/IJETT-V28P210

Saket Bhardwaj, Ajay Mittal. (2012). A survey on various edge detector techniques. Elsevier Procedia Technology, 4, 220 – 226. https://doi.org/10.1016/j.protcy.2012.05.033

Setayesh, M., Johnston, M., & Zhang, M. (2011). Edge and corner extraction using particle swarm optimization. In J. Li, editor, AI 2010: Advances in Artificial Intelligence Springer LNCS, 6464, 323–333.

Setayesh, M., Zhang, M., & Johnston, M. (2009). A new homogeneity- based approach to edge detection using PSO. IEEE 24th International Conference on Image and Vision Computing Press, 231–236. https://doi.org/10.1109/IVCNZ.2009.5378404

Shokhan, M. H. (2014). An efficient approach for improving canny edge detection algorithm. International Journal of Advances in Engineering & Technology, 7(1), 59-65.

Shrivakshan, G.T., Chandrasekar , C. (2012). A comparison of various edge detection techniques used in image processing. International Journal of Computer Science Issues, 9(1), 269-276.

Subashini, P., Dhivyaprabha, T. T., & Krishnaveni, M. (2016). Synergistic Fibroblast Optimization. Proceedings of Joint International Conference – ICAIECES-2016 & ICPCIT-, 2016, (pp. 1-6). Chennai: SRM University.

Xumin Liu, Zilong Duan, Xiaojun Wang & Weixiang Xu. (2016). An image edge detection algorithm based on improved wavelet transform. International Journal of Signal Processing, Image Processing and Pattern Recognition. 9(4), 435-442. https://doi.org/10.14257/ijsip.2016.9.4.38

Zhou Wang, Alan Bovik, C., Hamid Sheikh, R., & Eero Simoncelli, P. (2004). Image quality assessment: from error measurement to structural similarity. IEEE Transactions on Image Processing. 13(4), 600-612. https://doi.org/10.1109/TIP.2003.819861

Downloads

Published

2017-09-19

How to Cite

Krishnaveni, M., Subashini, P., & Dhivyaprabha, T. (2017). SYNERGISTIC FIBROBLAST OPTIMIZATION BASED BOUNDARY DETECTION IN TAMIL SIGN LANGUAGE IMAGES. MATTER: International Journal of Science and Technology, 3(2), 193–208. https://doi.org/10.20319/mijst.2017.32.193208