Computer Vision

Living Edition

Data Fusion

  • Ramanarayanan ViswanathanEmail author
Living reference work entry



Data fusion refers to combining data from multiple sources for achieving better understanding of a phenomenon of interest. Applications abound in engineering and applied sciences, including wireless sensor networks, computer vision, and biometrics.


In several fields, combining different sets of information has taken place, although a more systematic study for the fusion of data is emerging since a decade [1]. The human brain is an example of a complex system which integrates data or signals from different sensory preceptors in the body. Building a machine-based system that can meaningfully integrate data from different sources for better understanding of a phenomenon of interest is the challenge faced in many fields. Since data emerges from different sensors with varying accuracy and coverage factors, benefits of data fusion include improved system reliability and/or redundancy, extended coverage, and possible shorter response time....

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  1. 1.
    Varshney PK (1997) Mutisensor data fusion. Electron Commun Eng J 9(12):245–253CrossRefGoogle Scholar
  2. 2.
    Zheng S, Shi W-Z, Liu J, Zhu G-X, Tian J-W (2007) Multisource image fusion method using support value transform. IEEE Trans Image Process 16(7):1831–1839MathSciNetCrossRefGoogle Scholar
  3. 3.
    Snidaro L, Niu R, Foresti GL, Varshney PK (2007) Quality-based fusion of multiple video sensors for video surveillance. IEEE Trans Syst Man Cybern Part B Cybern 37(4):1044–1051CrossRefGoogle Scholar
  4. 4.
    Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23CrossRefGoogle Scholar
  5. 5.
    Chen H, Kirubarajan T, Bar-shalom Y (2003) Performance limits of track-to-track fusion versus centralized estimation: theory and application. IEEE Trans Aerosp Electron Syst 39(2):386–399CrossRefGoogle Scholar
  6. 6.
    Viswanathan R, Varshney PK (1997) Distributed detection with multiple sensors: part I-fundamentals (invited paper). Proc IEEE 85(1):54–63CrossRefGoogle Scholar
  7. 7.
    Blum RS, Kassam SA, Poor HV (1997) Distributed detection with multiple sensors: part II-advanced topics (invited paper). Proc IEEE 85(1):64–79CrossRefGoogle Scholar
  8. 8.
    Dasarathy BV (1994) Decision fusion. IEEE Computer Society Press, Los AlamitosGoogle Scholar
  9. 9.
    Willett P, Swaszek PF, Blum RS (2000) The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise. IEEE Trans Signal Process 48(12):3266–3279. Scholar
  10. 10.
    Kasasbeh H, Cao L, Viswanathan R (2019) Soft-decision based distributed detection with correlated sensing channels. IEEE Trans Aerospace Electron Syst 55(3):1435–1449. Scholar
  11. 11.
    Tay PW, Tsitsiklis JN, Win MZ (2008) On the subexponential decay of detection error probabilities in long tandems. IEEE Trans Inf Theory 54(10):4767–4771MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ribeiro A, Giannakis GB (2006) Bandwidth-constrained distributed estimation for wireless sensor networks- part I: Gaussian case. IEEE Trans Signal Process 54(3):1131–1143CrossRefGoogle Scholar
  13. 13.
    Chamberland J-F, Veeravalli VV (2007) Wireless sensors in distributed detection applications. IEEE Signal Process Mag 24(3):16–25CrossRefGoogle Scholar
  14. 14.
    Chen B, Jiang R, Kasetkesam T, Varshney PK (2004) Channel aware decision fusion in wireless sensor networks. IEEE Trans Signal Process 52(12):3454–3458MathSciNetCrossRefGoogle Scholar
  15. 15.
    Gandetto M, Regazzoni C (2007) Spectrum sensing: a distributed approach for cognitive terminals. IEEE J Sel Areas Commun 25(3):546–557CrossRefGoogle Scholar
  16. 16.
    Unnikrishnan J, Veeravalli VV (2008) Cooperative sensing for primary detection in cognitive radio. IEEE J Sel Top Signal Process 2(1):18–27CrossRefGoogle Scholar
  17. 17.
    Letaief KB, Zhang W (2009) Cooperative communications for cognitive radio networks. Proc IEEE 97(5):878–893CrossRefGoogle Scholar
  18. 18.
    Jain AK, Chellappa R, Draper SC, Memon N, Phillips PJ, Vetro A (2007) Signal processing for biometric systems (DSP forum). IEEE Signal Process Mag 24(6):146–152CrossRefGoogle Scholar
  19. 19.
    Basak J, Kate K, Tyagi V, Ratha N (2010) QPLC: a novel multimodal biometric score fusion method. In: Computer vision and pattern recognition workshops (CVPRW), San Francisco. IEEE Computer Society Conference, pp 46–52Google Scholar
  20. 20.
    Iyengar SG, Varshney PK, Damarla T (2011) A parametric copula-based framework for hypothesis testing using heterogeneous data. IEEE Trans Signal Process 59(5):2308–2319. Scholar
  21. 21.
    Paul PP, Gavrilova ML, Alhajj R (2014) Decision fusion for multimodal biometrics using social network analysis. IEEE Trans Syst Man Cybern Syst 44(11):1522–1533. Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of MississippiOxfordUSA

Section editors and affiliations

  • Rama Chellappa
    • 1
  1. 1.University of MarylandCollege ParkUSA