Abstract
Among all applications to monitor the safety and security of working environments, surveillance systems that use computer vision are the most efficient and intuitive in the manufacturing industry. This paper introduces a new technique of contrast enhancement for surveillance systems using computer vision. The histogram equalization method is a common and widespread image enhancement method which maximizes the contrast of the image. This contrast enhancement method usually improves the quality of images, but it can suffer from visual deterioration caused by excessive histogram modification. To overcome the limitations of conventional contrast enhancement methods, this paper introduces a new multi-local histogram transformation method for surveillance systems. This technique is based on the local histograms, which are separated from the overall histogram of the image, and the contrast of the image can be enhanced through two major processes: range reassignment of local histograms and local histogram equalization. The multi-local histogram transformation in this paper enhances the contrast of images, preventing excessive compression and extension of image histograms. The performance of the suggested contrast enhancement method is verified by the experiments in four different environments.
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References
Bandaranayake A. U., Pandit V., Agrawal D. P. (2012) Indoor link quality comparison of IEEE 802.11a channels in a multi-radio mesh network testbed. Journal of Information Processing Systems 8(1): 1–20
Capgemini, & IDG Global Solutions. (2008). Manufacturing in 2020. Envisioning a Future Characterised by Increased Internationalisation, Collaboration and Complexity. Capgemini. http://www.capgemini.com/insights-and-resources/by-publication/manufacturing_in_2020/. Accessed 15 September 2011.
Chen S., Ramli A. R. (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics 49(4): 1301–1309
Chen S., Ramli A. R. (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics 49(4): 1310–1319
Chong R. M., Tanaka T. (2010) Motion Blur identification using maxima locations for blind colour image restoration. Journal of Convergence 1(1): 49–56
Choudhary A. K., Harding J. A., Tiwari M. K. (2009) Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5): 501–521
Craik K. J. W. (1938) The effect of adaptation on differential brightness discrimination. The Journal of Physiology 92: 406–421
Desforges X., Habbadi A., Geneste L., Soler F. (2004) Distributed machining control and monitoring using smart sensors/actuators. Journal of Intelligent Manufacturing 15(1): 39–53
Evdokimov S., Fabian B., Günther O., Ivantysynova L., Ziekow H. (2011) RFID and the internet of things: Technology, applications, and security challenges. Foundations and Trends in Technology, Information and Operations Management 4(2): 105–185
Gonzalez R. C., Woods R. E. (2002) Digital image processing. Prentice Hall, New Jersey
Ho A. H., Ho Y. H., Hua K. A., Villafane R., Chao H.-C. (2010) An efficient broadcast technique for vehicular networks. Journal of Information Processing Systems 7(2): 221–240
Hou T.-H., Huang C.-C. (2004) Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction. Journal of Intelligent Manufacturing 15(3): 395–408
Hu, J., Wang, D., Ding, Y., Zhang, J., & Tan, H. (2010). Design and implementation of intelligent RFID security authentication system. In Proceedings of 2010 IEEE international conference on RFID-technology and applications, RFID-TA 2010, (pp. 286–290).
Huang C., Cheng R.-H., Chen S.-R., Li C.-I. (2010) Enhancing network availability by tolerance control in multi-sink wireless sensor networks. Journal of Convergence 1(1): 15–22
Ibrahim H., Kong N. S. P. (2007) Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics 53(4): 1752–1758
Kabir H., Al-Wadud A., Chae O. (2010) Brightness preserving image contrast enhancement using weighted mixture of global and local transformation functions. International Arab Journal of Information Technology 7(4): 403–410
Kim I. S., Choi H. S., Yi K. M., Choi J. Y., Kong S. G. (2010) Intelligent visual surveillance—A survey. International Journal of Control, Automation and Systems 8(5): 926–939
Kim Y. T. (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics 43(1): 1–8
Kumar D., Aseri T. C., Patel R. B. (2011) Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks. International Journal of Information Technology, Communications and Convergence 1(2): 130–145
Ooi C. H., Kong N. P., Ibrahim H. (2009) Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Transactions on Consumer Electronics 55(4): 2072–2080
Ooi C. H., Isa N. A. M. (2010) Adaptive contrast enhancement methods with brightness preserving. IEEE Transactions on Consumer Electronics 56(4): 2543–2551
Pizer S. M., Amburn E. P., Austin J. D., Cromartie R., Geselowitz A., Greer T. et al (1987) Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing 39(3): 355–368
Sahebjamnia N., Mahdavi I., Cho N. (2010) Designing a new model of distributed quality control for sub-assemble products based on the intelligent web information system. Journal of Intelligent Manufacturing 21(4): 511–523
Srivastava A., Gupta J. P. (2010) New methodologies for security risk assessment of oil and gas industry. Process Safety and Environmental Protection 88(6): 407–412
Wang X. (2011) Application of IOT technologies in campus security system. Advanced Materials Research 268-270: 1884–1887
Xie B., Kumar A., Zhao D., Reddy R., He B. (2010) On secure communication in integrated heterogeneous wireless networks. International Journal of Information Technology, Communications and Convergence 1(1): 4–23
Xu Y., Ge M. (2004) Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing 15(3): 337–350
Yang S. J., Oh J. H., Park Y. J. (2003) Contrast enhancement using histogram equalization with bin underflow and bin overflow. IEEE International Conference on Image Processing 1: 881–884
Zuo, C., Chen, Q., & Sui, X. (2012). Range limited bi-histogram equalization for image contrast enhancement.Optik, doi:10.1016/j.ijleo.2011.12.057.
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Kwak, HJ., Park, GT. Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation. J Intell Manuf 25, 303–318 (2014). https://doi.org/10.1007/s10845-012-0663-4
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DOI: https://doi.org/10.1007/s10845-012-0663-4