Letters Extraction in Sign Board Using Various Optimization Techniques
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Accurate localization of randomly deployed sensor nodes is critically important in wireless sensor networks (WSNs) deployed for monitoring and tracking applications. The localization challenge has been posed as a multidimensional global optimization problem in earlier literature. Many swarm intelligence algorithms have been proposed for accurate localization. The untapped vast potential of the BFO algorithm has inspired the research presented in this paper. The ABC algorithm has been investigated as a tool for anchor-assisted sensor localization in WSNs. Results of Matlab simulation of BFO-based multistage localization has been presented. Further, the results are compared with those of the localization method based on the particle swarm optimization (PSO) algorithm. A comparison of the performances of BFO and PSO algorithms has been presented in terms of the number of nodes localized, localization accuracy and the computation time. The results show that the ABC algorithm delivers higher accuracy of localization than the PSO algorithm does; but, it takes longer to converge. This results in a tradeoff between speed and accuracy of localization in WSNs.
Kai Chen (2016), Effective Candidate Component Extraction for Text Localization in Born-Digital Images by Combining Text Contours and Stroke Interior Regions, 12th IAPR Workshop on Document Analysis Systems (DAS),pp 352 – 357
Yuanyuan Feng (2015) Scene text localization using extremal regions and Corner-HOG feature” IEEE International Conference on Robotics and Biomimetics (ROBIO) pp 881 – 886.
Kuntpong Woraratpanya (2014) , Text-background decomposition for thai text localization and recognition in natural scenes 6th International Conference on Information Technology and Electrical Engineering (ICITEE),pp1-6
Khalid Iqbal (2014), Bayesian network scores based text localization in scene images, International Joint Conference on Neural Networks (IJCNN), 2218 – 2225.
Lukas Neumann (2015), Real-Time Lexicon-Free Scene Text Localization and Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 38 , Issue 9 pp 1872 – 1885.
Mohammad Shrif Uddin, Madeena Sultana, Tanzila Rahman, and Umme Sayma Busra (2012), Extraction of texts from ascene image using morphology based approach, IEEE Transactions on image processing, Vol.10, pp.306-309.
Nirmala Shivananda and P. Nagabhushan (2009),Separation of Fore ground Text from Complex Background in Color Document Images, IEEE Transactions on Image Processing, vol.10, pp.306-309.
Partha Pratim Roy, Josep Llad´os and Umapada Pal (2007), Text/Graphics Separationin Color Maps ,IEEE Transactions on Image Processing, vol .7, pp.545-551.
D. Crandall, S. Antani, and R. Kasturi (2001), Robust Detection of Stylized Text Events in Digital Video, Proceedings of International Conference on Document Analysis and Recognition, pp. 865-869.
R. Chandrasekaran, R.M. Chandrasekaran, P.Natarajan (2012) ,Text localization and extraction in images using mathematical morphology and SVM,IEEE International Conference(ICAESM) , pp 265-269
J. Canny (1986) A Computational Approach to Edge Detection. IEEE Trans. PAMI, 8(6):679–698.
H. Chen, S. S. Tsai, G. Schroth, D. M. Chen, R. Grzeszczuk, and B. Girod (2011) ,Robust text detection in natural images with edge-enhanced maximally stable extremal regions , In Proc. ICIP, pages 2609–2612
Clavelli, D. Karatzas, and J. Llados (2010) , A Framework for the Assessment of Text extraction Algorithms on Complex Colour Images , In Proc. 9th DAS, pages 19–28.
T. E. de Campos et.al (2009) Character recognition in natural images , In Proc. VISAPP, pp 220-226
R. O. Duda, P. E. Hart, and D. G. Stork (2006) .Pattern Classification. Wiley, 2 edition ,pp 434-439
B. Epshtein, E. Ofek, and Y.Wexler (2010). Detecting text in natural scenes with stroke width transform. In Proc. 23rd CVPR, pages 2963–2970.
B. Gatos, K. Ntirogiannis, and I. Pratikakis (2009), Document Image BinarizationContest ,In Proc. 10th ICDAR, pages 1375-1382.
GNU Image Manipulation Program (GIMP). http://www.gimp.org/.
R. C. Gonzalez and R. E. Woods (2002) ,Digital Image Processing. Prentice Hall, 2 edition.
