Skip to main content
SpringerLink
Log in

Fused Image Separation with Scatter Graphical Method

  • Conference paper
  • First Online:
Harmony Search and Nature Inspired Optimization Algorithms

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 741))

Abstract

Image fusion and its separation is a frequently arising issue in Image processing field. In this paper, we have described image fusion and its Separation using Scatter graphical method and Joint Probability Density Function. Fused image separation using Scatter Graphical Method depend on Joint Probability density function of fused image. This technique gives batter result of other technique based on Signal Interference ratio and peak signal-to-noise ratio.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Singh, D.K., Tripathi, S., Kalra, P.K.: Separation of image mixture using complex ICA. In: Proceedings of ASID ’06, pp. 8–12. New Delhi

    Google Scholar 

  2. Tonazzini, A., Bedini, L., Salerno, E.: A Markov model for blind image separation by a mean-field EM algorithm. IEEE Trans. Image Process. 15(2) (2006)

    Google Scholar 

  3. Kumari, M., Wajid, M.: Source separation of independent components. LRC, JUIT, 2013, SPR 621 KUM, SPM1327

    Google Scholar 

  4. Carasso, D., Vizel, E., Zeevi, Y.Y.: Blind source separation using mixtures scatter plot properties. In: 2009 16th International Conference on Digital Signal Processing. IEEE (2009)

    Google Scholar 

  5. Sternberg, S.R.: Biomedical image processing. Computer 16(1), 22–34 (1983)

    Article  Google Scholar 

  6. Parra, L., Sajda, P.: Blind source separation via generalized eigenvalue decomposition. J. Mach. Learn. Res. 4, 1261–1269 (2003) Submitted 10/02; Published 12/03

    Google Scholar 

  7. Zarzoso, V., Comon, P.: Robust independent component analysis for blind source separation and extraction with application in electrocardiography. In: 30th Annual International IEEE EMBS Conference Vancouver. British Columbia, Canada, August 20–24, 2008

    Google Scholar 

  8. Choras, R.S.: Image feature extraction techniques and their applications for CBIR and biometrics systems. Int. J. Biol. Biomed. Eng. 1(1), 6–16 (2007)

    Google Scholar 

  9. Ziquan, H.: Algebraic feature extraction of image forrecognition. Pattern Recogn. 24(3), 211–219 (1991)

    Article  Google Scholar 

  10. Zou, W., Li, Y., Lo, K.C., Chi, Z.: Improvement of image classification with wavelet and independent component analysis (ICA) based on a structured neural network. In: 2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16–21, 2006

    Google Scholar 

  11. Chye, K.C., Mukherjee, J., Mitra, S.K.: New efficient methods of image compression in digital cameras with color filter array. Consum. Electron. IEEE Trans. 49(4), 1448–1456 (2003)

    Article  Google Scholar 

  12. Jadhav, S.D., Bhalchandra, A.S.: Blind source separation based robust digital image watermarking using wavelet domain embedding. In: 2010 IEEE Conference on Cybernetics and Intelligent Systems (CIS). IEEE (2010)

    Google Scholar 

  13. kundur, D., Hatzinakos, D.: A robust digital image watermarking scheme using the wavelet-based fusion. In: Image Processing, International Conference on vol. 1. IEEE Computer Society (1997)

    Google Scholar 

  14. Huadong, D., Yongqi, W., Yaming, C.: Studies on cloud detection of atmospheric remote sensing image using ICA algorithm. In: 2nd International Congress on Image and Signal Processing, 2009 CISP’09. IEEE (2009)

    Google Scholar 

  15. Ye, N. (ed.): The Handbook of Data Mining, vol. 24. Lawrence Erlbaum Associates, Publishers (2003)

    Google Scholar 

  16. Zhao, Q., Jain, A.K.: Model based separation of overlapping latent fingerprints. Inf. Forensics Secur. IEEE Trans. 7(3), 904–918 (2012)

    Article  Google Scholar 

  17. Tonazzini, A., Bedini, L., Salerno, E.: A Markov model for blind image separation by a mean-field EM algorithm. Image Process. IEEE Trans. 15(2), 473–482 (2006)

    Article  MathSciNet  Google Scholar 

  18. Chen, F., et al.: Separating overlapped fingerprints. Inf. Forensics Secur. IEEE Trans. 6(2), 346–359 (2011)

    Article  Google Scholar 

  19. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, vol. 46. Wiley (2004)

    Google Scholar 

  20. Hyvarinen, A.: Blind source separation by nonstationarity of variance: a cumulant-based approach. Neural Networks IEEE Trans. 12(6), 1471–1474 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rustan Ji Institute of Technology, Tekanpur, India

    Mayank Satya Prakash Sharma, Nikhil Paliwal & Prashant Shrivastava

  2. ITM University Gwalior, Gwalior, India

    Ranjeet Singh Tomar

Authors
  1. Mayank Satya Prakash Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ranjeet Singh Tomar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikhil Paliwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prashant Shrivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mayank Satya Prakash Sharma .

Editor information

Editors and Affiliations

  1. School of Engineering and Technology, BML Munjal University, Gurgaon, Haryana, India

    Neha Yadav

  2. Department of Sciences and Humanities, National Institute of Technology, Srinagar, Uttarakhand, India

    Anupam Yadav

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, M.S.P., Tomar, R.S., Paliwal, N., Shrivastava, P. (2019). Fused Image Separation with Scatter Graphical Method. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_13

Download citation

Publish with us

Policies and ethics

Search

Navigation