Novel Region Growing Mechanism for Object Detection in a Complex Background

  • Tamanna SahooEmail author
  • Bibhuprasad Mohanty
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 665)


Object detection is vital for visual processing applications. In this work, the desired object in an image is detected by the help of the wavelet coefficient feature (WCF) extraction and region growing technique. The region growing technique is based upon the appropriate selection of seed block computation and adjacency thresholding technique. The novelty of the proposed work is based on computation of seed block using WCF from the dynamics of the image instead of an image itself. Haar filter has been applied to transform the image after two level of decomposition for WCF extraction and to take care of the reduction in time complexity of the system. The extensive simulation-based experiment demonstrates the proposed methodology efficiently detects the object even in the presence of complex or cluttered (dynamic) background.


Object detection Wavelet coefficient feature Seed block Region growing 


  1. 1.
    Pulla Rao, C., Guruva Reddy, A., Rama Rao, C.B.: Target detection using multi resolution analysis for camouflaged images. Int. J. Cybern. Inf. 5(4), 135–147 (2016)Google Scholar
  2. 2.
    Li, Z.Q., Fang, T., Huo, H.: A saliency model based on wavelet transform and visual attention. Sci. China Inf. Sci. 53(4), 738–751 (2010)CrossRefGoogle Scholar
  3. 3.
    Arivazhagan, S., Ganesan, L.: Automatic target detection using wavelet transform. EURASIP J. Appl. Sig. Process. 2004(17), 2663–2674 (2004)zbMATHGoogle Scholar
  4. 4.
    Sahoo, T., Mohanty, B.: A systematic review on visual attention and its application. Indian J. Public Health Res. Dev. 9(11), 2278–2286 (2018)CrossRefGoogle Scholar
  5. 5.
    Pan, Y., Chen, Y., Fu, Q., Zhang, P., Xu, X.: Study on the camouflaged target detection method based on 3D convexity. Proc. Mod. Appl. Sci. 5, 152–157 (2011)Google Scholar
  6. 6.
    Tsapatsoulis, N., Rapantzikos, K.: Wavelet based estimation of saliency maps in visual attention algorithms. In: LNCS, vol. 4132, pp. 538–547. Springer, Berlin (2006)Google Scholar
  7. 7.
    Haralick, R.M., Shanmugan, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-8(6), 610–621 (1973)CrossRefGoogle Scholar
  8. 8.
    Chen, P.C., Pavlidis, T.: Segmentation by texture using correlation. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5, 64–69 (1983)CrossRefGoogle Scholar
  9. 9.
    Laws, K.I.: Textured image segmentation. Ph.D. dissertation, Rept. 940, Image Processing Institute, University of Southern California (1980)Google Scholar
  10. 10.
    Unser, M.: Local linear transforms for texture measurements. Sig. Process. 11(1), 61–79 (1986)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kashyap, R.L., Chellappa, R., Khotanzad, A.: Texture classification using features derived from random field models. Pattern Recogn. Lett. 1, 43–50 (1982)CrossRefGoogle Scholar
  12. 12.
    Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process. 4(11), 1549–1560 (1995)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Changand, T., Kuo, C.-C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)Google Scholar
  14. 14.
    Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recogn. Lett. 24(9–10), 1513–1521 (2003)CrossRefGoogle Scholar
  15. 15.
    Kubota, T., Huntsberger, T.L., Alford, C.O.: A vision system with real-time feature extractor and relaxation network. Int. J. Pattern Recogn. Artif. Intell. 12(3), 335–354 (1998)CrossRefGoogle Scholar
  16. 16.
    Huntsberger, T.L., Jawerth, B.D.: Wavelet based automatic target detection and recognition. Annual Technical Report, University Research Initiative Program for Combat Readiness, University of South Carolina, Columbia, SC, USA (1998)Google Scholar
  17. 17.
    Huntsberger, T.L., Jawerth, B.D.: Wavelet based algorithms for acoustic and non-acoustic antisubmarine warfare. Annual Technical Report, University Research Initiative Program for Combat Readiness, University of South Carolina, Columbia, SC, USA (1998)Google Scholar
  18. 18.
    Tian, Y., Qi, H., Wang, X.: Target detection and classification using seismic signal processing in unattended ground sensor systems. In: Procedding IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP’02), vol. 4, p. 4172, Orlando, FL, USA, May 2002Google Scholar
  19. 19.
    Boccignone, G., Chianese, A., Picariello, A.: Using Renyi’s information and wavelets for target detection: an application to mammograms. Pattern Anal. Appl. 3(4), 303–313 (2000)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Espinal, F., Huntsberger, T.L., Jawerth, B.D., Kubota, T.: Wavelet-based fractal signature analysis for automatic target recognition. Opt. Eng. 37(1), 166–174 (1998)CrossRefGoogle Scholar
  21. 21.
    Sastry, C.S., Pujari, A.K., Deekshatulu, B.L., Bhagvati, C.: A Wavelet based Multiresolution algorithm for rotation invariant feature extraction. Proc. Pattern Recogn. Lett. 25, 1845–1855 (2004)CrossRefGoogle Scholar
  22. 22.
    Arivazhagan, S.: Automatic target detection using wavelet transform. EURASIP J. Appl. Sig. Process. (2004)Google Scholar
  23. 23.
    Sahoo, T., Mohanty, B.: Moving object detection using background subtraction in wavelet domain. In: 2nd International Conference on Data Science and Business Analytics (ICDSBA), Sept 2018Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringInstitute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia

Personalised recommendations