International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Multi-Level Segmentation of Infrared Images with Region of Interest Extraction

  • Yeom, Seokwon (School of Computer and Communication Engineering, Daegu University)
  • Received : 2016.07.01
  • Accepted : 2016.11.23
  • Published : 2016.12.12
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Abstract

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

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References

  1. S. Erturk, "Region of interest extraction in infrared images using one-bit transform," IEEE Signal Processing Letters, vol. 20, no. 10, pp. 952-955, 2013. http://dx.doi.org/10.1109/LSP.2013.2274637
  2. D. K. Shin and Y. S. Moon, "Extraction of infrared target based on Gaussian mixture model," IEEK Transactions on Smart Processing and Computing, vol. 2, no. 6, pp.332-338, 2013.
  3. C.W. Park, J. M. Lee, Y. M. Kim, Y. Kim, T. L. Song, K. T. Park, and Y. S. Moon, "Extracting targets from regions-ofinterest in infrared images using a 2-D histogram," Optical Engineering, vol. 50, no. 2, pp. 1-5, 2011. http://dx.doi.org/10.1117/1.3536471
  4. T. J. Ramirez-Rozo, J. C. Garcia-Alvarez, and C. G. Castellanos-Dominguez, "Infrared thermal image segmentation using expectation-maximization-based clustering," in Proceedings of 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision, Medellin, Colombia, 2012, pp. 1-4. http://dx.doi.org/10.1109/STSIVA.2012.6340586
  5. M. Teutsch, T. Mueller, M. Huber, and J. Beyerer, "Low resolution person detection with a moving thermal infrared camera by hot spot classification," in Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, 2014, pp. 209-216. http://dx.doi.org/10.1109/CVPRW.2014.40
  6. S. Greenberg, S. R. Rotman, H. Guterman, S. Zilberman, and A. Gens, "Region-of-interest-based algorithm for automatic target detection in infrared images," Optical Engineering, vol. 44, no. 7, pp. 1-10, 2005. http://dx.doi.org/10.1117/1.1951547
  7. H. Y. Lee, T. H. Kim, and K. H. Park, "Target extraction in forward-looking infrared images using fuzzy thresholding via local region analysis," Optical Review, vol. 18, no. 5, pp. 383-388, 2011. http://dx.doi.org/10.1007/s10043-011-0073-4
  8. T. Gandhi and M. M. Trivedi, "Image based estimation of pedestrian orientation for improving path prediction," in Proceedings of 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands, 2008, pp. 506-511. http://dx.doi.org/10.1109/IVS.2008.4621257
  9. F. Xu, X. Liu, and K. Fujimura, "Pedestrian detection and tracking with night vision," IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 1, pp. 63-71, 2005. http://dx.doi.org/10.1109/TITS.2004.838222
  10. J. Ge, Y. Luo, and G. Tei, "Real-time pedestrian detection and tracking at nighttime for driver-assistance systems," IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 2, pp. 283-298, 2009. http://dx.doi.org/10.1109/TITS.2009.2018961
  11. Y. Fang, K. Yamada, Y. Ninomiya, B. K. P. Horn, and I. Masaki, "A shape-independent method for pedestrian detection with far-infrared images," IEEE Transactions on Vehicular Technology, vol. 53, no. 6, pp. 1679-1697, 2004. http://dx.doi.org/10.1109/TVT.2004.834875
  12. M. S. Alam and A. Bal, "Improved multiple target tracking via global motion compensation and optoelectronic correlation," IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 522-529, 2007. http://dx.doi.org/10.1109/TIE.2006.885513
  13. D. S. Lee and S. Yeom, "Infrared image segmentation with Gaussian mixture modeling," Proceedings of SPIE, vol. 8355, article no. 83551J, 2012. http://dx.doi.org/10.1117/12.919615
  14. H. Eum, J. Bae, C. Yoon, and E. Kim, "Ship detection using edge-based segmentation and histogram of oriented gradient with ship size ratio," International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 4, pp. 251-259, 2015. http://dx.doi.org/10.5391/IJFIS.2015.15.4.251
  15. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford: Oxford University Press, 1995.
  16. S. Yeom, D. S. Lee, J. Y. Son, M. K. Jung, Y. Jang, S. W. Jung, and S. J. Lee, "Real-time outdoor concealedobject detection with passive millimeter wave imaging," Optics Express, vol. 19, no. 3, pp. 2530-2536, 2011. http://dx.doi.org/10.1364/OE.19.002530
  17. A. Gersho and R. M. Gray, "Predictive quantization," in Vector Quantization and Signal Compression, A. Gersho and R. M. Gray, Eds. Boston, MA: Springer, 1992, pp. 203-223. http://dx.doi.org/10.1007/978-1-4615-3626-07
  18. W. A. Yasnoff, J. K. Mui, and J. W. Bacus, "Error measures for scene segmentation," Pattern Recognition, vol. 9, no. 4, pp. 217-231, 1977. http://dx.doi.org/10.1016/0031-3203(77)90006-1
  19. J. W. Davis and M. A. Keck, "A two-stage template approach to person detection in thermal imagery," in Proceedings of Seventh IEEE Workshops on Application of Computer Vision, Breckenridge, CO, 2005. http://dx.doi.org/10.1109/ACVMOT.2005.14
  20. IEEE OTCBVS WS Series Bench and R. Miezianko, "Terravic research infrared database," Available http://vciplokstate.org/pbvs/bench/

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