Segmentation of Human Body Image

Authors

  • Prof. T. R. Harinkhede  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • P. R. Girhepunje  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • S. S. Dekate  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • D. S. Dable  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • A. R. Pawar  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • A. P. Korde  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • R. P. Yenurkar  Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India
  • D. D. Ganvir   Department of ETC, RTMNU, SRMCEW, Nagpur, Maharashtra, India

Keywords:

Adaptive Skin Detection, Anthropometric Constraints, Human Body Segmentation, And Multilevel Image Segmentation

Abstract

Human body image segmentation is a difficult process that can use in important application,like scene understanding and activity recognition. In order to deal with the highly dimensional pose space, scene complexity, and various human appearances, the majority of have being works require computationally composite training and template matching processes. We have to converse a bottom-up methodology for segmentation of human bodies from single images, in the case of almost upright poses in cluttered environments. The position, dimensions, and colour of the face are used for the localization of the human body, construction of the models for the upper and lower body according to anthropomertric constraints, and approximation of the skin colour. Different levels of segmentation granularity are combined to extract the pose with highest ability. The segments that belong to the human body arise through the joint approximation of the upper and lower during the body part search phases, which make easy the need for exact shape matching.

References

    2. M. Andriluka, S. Roth, and B. Schiele, “Pictorial structures revisited: People detection and articulated pose estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 1014–1021.
  2. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” Int. J. Comput. Vis., vol. 88, no. 2, pp. 303–338, 2010.
  3. V. Ferrari, M. Marin-Jimenez, and A. Zisserman, “Progressive search space reduction for human pose estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2008, pp. 1–8.
  4. M. P. Kumar, A. Zisserman, and P. H. Torr, “Efficient discriminative learning of parts-based models,” in Proc. IEEE 12th Int. Conf. Comput. Vis., 2009, pp. 552–559.
  5. V. Delaitre, I. Laptev, and J. Sivic, “Recognizing human actions in still images: A study of bag-of-features and part-based representations,” in Proc. IEEE Brit. Mach. Vis. Conf., 2010.
  6. A. Gupta, A. Kembhavi, and L. S. Davis, “Observing human-object interactions: Using spatial and functional compatibility for recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 10, pp. 1775–1789, Oct. 2009.
  7. B. Yao and L. Fei-Fei, “Grouplet: A structured image representation for recognizing human and object interactions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 9–16.
  8. P. Buehler, M. Everingham, D. P. Huttenlocher, and A. Zisserman, “Long term arm and hand tracking for continuous sign language TV broadcasts,” in Proc. 19th Brit. Mach. Vis. Conf., 2008, pp. 1105–1114.
  9. A. Farhadi and D. Forsyth, “Aligning ASL for statistical translation using a discriminative word model,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2006, pp. 1471–1476.
  10. L. Zhao and L. S. Davis, “Iterative figure-ground discrimination,” in Proc. 17th Int. Conf. Pattern Recog., 2004, pp. 67–70.
  11. L. Grady, “Random walks for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 11, pp. 1768–1783, Nov. 2006.
  12. C. Rother, V. Kolmogorov, and A. Blake, “Grabcut: Interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph., vol. 23, no. 3, pp. 309–314, Aug. 2004.
  13. V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman, “Geodesic star convexity for interactive image segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 3129–3136.
  14. Y. Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary & region segmentation of objects in ND images,” in Proc. IEEE 8th Int. Conf. Comput. Vis., 2001, pp. 105–112.
  15. M. P. Kumar, P. H. S. Ton, and A. Zisserman, “Obj cut,” in Proc. IEEE Comput. Soci. Conf. Comput. Vision Pattern Recog., 2005, pp. 18–25.
  16. S. Li, H. Lu, and L. Zhang, “Arbitrary body segmentation in static images,” Pattern Recog., vol. 45, no. 9, pp. 3402–3413, 2012.
  17. L. Huang, S. Tang, Y. Zhang, S. Lian, and S. Lin, “Robust human body segmentation based on part appearance and spatial constraint,” Neurocomputing, vol. 118, pp. 191–202, 2013. 338 IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, VOL. 45, NO. 3, JUNE 2015
  18. P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis., vol. 61, no. 1, pp. 55–79, 2005.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2017-02-28

Issue

Volume 3, Issue 2, January-February-2017

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. T. R. Harinkhede, P. R. Girhepunje, S. S. Dekate, D. S. Dable, A. R. Pawar, A. P. Korde, R. P. Yenurkar, D. D. Ganvir , " Segmentation of Human Body Image, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 2, pp.110-113, January-February-2017.