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Reseach Article

Entropy Correlation Coefficient Technique for Visual Data in Multimedia Sensor Network

by Subhash S., Gururaj H. L., Ramesh B.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 2
Year of Publication: 2016
Authors: Subhash S., Gururaj H. L., Ramesh B.
10.5120/ijca2016911002

Subhash S., Gururaj H. L., Ramesh B. . Entropy Correlation Coefficient Technique for Visual Data in Multimedia Sensor Network. International Journal of Computer Applications. 148, 2 ( Aug 2016), 1-6. DOI=10.5120/ijca2016911002

@article{ 10.5120/ijca2016911002,
author = { Subhash S., Gururaj H. L., Ramesh B. },
title = { Entropy Correlation Coefficient Technique for Visual Data in Multimedia Sensor Network },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 2 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number2/25726-2016911002/ },
doi = { 10.5120/ijca2016911002 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:52:11.845720+05:30
%A Subhash S.
%A Gururaj H. L.
%A Ramesh B.
%T Entropy Correlation Coefficient Technique for Visual Data in Multimedia Sensor Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 2
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Wireless Multimedia Sensor Network consists cameras at the sensor node in visual data application. These camera sensor devices capture their observations limited by the FoV as an image. There is a correlation between images captured by multiple cameras at a particular area. This leads to the redundant data transmission in the network. As the sensor node are battery powered and resource limited. Hence these scare resources should be used efficiently. This paper focuses on implementation of entropy correlation coefficient model in WMSN for visual data. The implementation of entropy, joint entropy and mutual information is performed to estimate an ECC which describes correlation characteristics of images observed by camera with overlapped sensing area. SIFT algorithm is used to perform the merging operations between two images. Using the RANSAC algorithm features are matched and the homography between two images are found. The results obtained satisfies the relation between ECC and Joint entropy.

References
  1. Dai and Akyildiz, "A spatial connection model for visual data in remote sight and sound sensor systems," IEEE TRANSACTIONS ON MULTIMEDIA, vol. 11, Oct 2009.
  2. Dai and Akyildiz, "A spatial connection based picture pressure structure for remote media sensor systems," IEEE TRANSACTIONS ON MULTIMEDIA, vol. 13, April 2011.
  3. Lowe, "Particular picture highlights from scale-invariant keypoints," International Journal of Computer Vision, vol. 60.
  4. Ma and Liu, "Relationship based video preparing in video sensor systems," IEEE Int. Conf.Wireless Networks,Communications and Mobile Computing, vol. 2.
  5. D. Devarajan, Z. Cheng, and R. J. Radke, "Aligning circulated camera systems," Proc. IEEE, vol. 96, Oct 2008.
  6. Wiegand, Sullivan, Bjntegaard, and A. Luthra, "Diagram of the h.264/avc video coding standard," IEEE Trans. Circuits Syst. Video Technol., vol. 13, July 2003.
  7. Wu and Abouzeid, "Vitality proficient circulated picture pressure in asset compelled multihop remote systems," ELSEVIER, Computer Communications, vol. 28, Sep 2005.
  8. Wang, Bovik, Sheik, and Simoncelli, "Picture quality evaluation: From blunder perceivability to basic closeness," IEEE Trans. Picture Process., vol. 13, April 2004.
  9. R. Puri, A. Majumdar, and K. Ramchandran, "Crystal: A video coding worldview with movement estimation at the decoder," IEEE Trans. Picture Process., vol. 16, Oct 2007.
  10. Pluim, . Maintz, and Viergever, "Common data based enlistment of restorative pictures: A review," IEEE Trans.Med. Imag., vol. 22, no. 8, 2010.
  11. Studholme,. Slope, and Hawkes, "A cover invariant entropy measure of 3d restorative picture arrangement," Pattern Recognition, vol. 32, no. 1.
  12. Jain, Murty, and P. J. Flynn, "Information grouping: An audit," ACM Comput. Surv., Sep 1999.
  13. A. Fathimaa, R.Karthikb, and V.Vaidehic, "Picture sewing with consolidated minute invariants and filter highlights," Pattern Recognition, vol. 32, no. 1.
  14. C. E. Shannon, "A numerical hypothesis of correspondence," The Mathematical Theory of Communication, 1949.
  15. R. Szeliski, “Image alignment and stitching,” in JA Tutorial1, Microsoft Re-search, Microsoft Corporation, (http://www.research.microsoft.com), 204.
Index Terms

Computer Science
Information Sciences

Keywords

Source image entropy mutual Information joint entropy ECC SIFT.