Integration of optical and acoustical imaging sensors for underwater applications

  • Goffredo G. Pieroni
  • Gian Luca Foresti
  • Vittorio Murino
Special Session on European Projects
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


This paper describes the main scientific and technical aspects foreseen for the development of the project BE-2013 titled VENICE (Virtual ENvironment interface by sensory integration for Inspection and manipulation Control in multifunctional underwater vehicles), financed by the European Commission under the programme BRITE-Euram III, related to the role of the Department of Mathematics and Computer Science of the University of Udine. This project is devoted to the study and development of methodologies for optimising acoustical and optical sensors' functioning and integrating related data for the formation of an accurate virtual environment aimed at supporting navigation, inspection, and maintenance/repair tasks of multifunctional remotely operated underwater vehicles.


Virtual Environment Underwater Vehicle Back Propagation Neural Network Hough Transform Underwater Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    D.R.Broome, D. Langrock, “Subsea Non-Destructive Testing Inspection by Remotely-Operated Vehicles,” 32nd Annual British Conf. on NDT, Cardiff, Sept. 1989, pp. 14–16.Google Scholar
  2. [2]
    R.K.Hansen, D.Sjong, L.Bjorno, “Underwater Robotics: a Flexible Robot Concept with Novel Acoustic Sensing Abilities,” Proc. Underw. Tech. Conf., Bergen, April 1988, pp. 369–379.Google Scholar
  3. [3]
    M.Sykes, A.R.Greig, D.R.Broome, “A Multisensor System for Improved Fault Detection and Classification,” IEEE Coll. Multisen. data Fusion, London, May 1992, vol. 1992/127, pp. 6/1–6/3Google Scholar
  4. [4]
    J.S.Chu, L.A.Lieberman, P.Downes, “Automatic Camera Control for AUVs: A Comparison of Image Assessment Methods”, 1992 Symp. AUV Techn., Washington, June 1992, pp.191–201.Google Scholar
  5. [5]
    V.Murino, C.S.Regazzoni, G.L.Foresti, “A Distributed Probabilistic System for Adaptive Regulation of Image Processing Parameters”, IEEE Transaction on Systems, Man, and Cybernetics, Vol. 26, No. 2, February 1996, pp. 1–22.Google Scholar
  6. [6]
    G.G.Pieroni, S.P.Tripathy, “A Multiresolution Approach for Segmenting Surfaces”, Issue on Machine Vision, G.G. Pieroni Editor, Springer, 1989.Google Scholar
  7. [7]
    W.E.L.Grimson, Object Recognition by Computer, The MIT Press, Cambridge, 1990.Google Scholar
  8. [8]
    R.K.Hansen, P.A.Andersen, “3D Acoustic Camera for Underwater Imaging”, Acoustical Imaging, vol. 20, pp. 723–727, 1993.Google Scholar
  9. [9]
    S.G.Johnson, M.A.Deaett, “The Application of Automated Recognition Techniques to Side-Scan Sonar Imagery”, IEEE Jour. Oceanic Engineering, vol. 19, pp.138–141, January 1994.CrossRefGoogle Scholar
  10. [10]
    G.G.Pieroni, V.Cantoni, O.Johnson, N.Nalato, “Use of the Hough Transform and Back Propagation Neural Networks to Perform 3-D Scene Recognition,” 2nd Ital. Work. Paral. Architec. Neural Net., Vietri sul Mare, April 1989.Google Scholar
  11. [11]
    R.C.Luo, M.G.Kay, “Multisensor Integration and Fusion in Intelligent Systems,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 19, No. 5, pp. 901–931, Sept./October 1989.Google Scholar
  12. [12]
    P.Perona, J.Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, No. 7, pp. 629–639, July 1990.CrossRefGoogle Scholar
  13. [13]
    J.Canny, “A computational approach to edge detection”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, Nov. 1986.Google Scholar
  14. [14]
    J.B.Burns, A.R.Hanson, E.M.Riseman, “Extracting Straight Lines”, IEEE Transactions on Pattern Analysis and machine Intelligence, Vol. 8, no. 4, pp. 425–455, July 1986.Google Scholar
  15. [15]
    P. Horn, Robot Vision, The MIT Press, Cambridge, MA, 1986.Google Scholar
  16. [16]
    K. Torrance, E. Sparrow, “Theory of off-specular reflection from roughness surfaces”, Jour. Optical Society of America, vol. 57, no. 9, pp. 1105–1114, 1967.Google Scholar
  17. [17]
    H. Tagare, R. DeFigueiredo, “A framework for the construction of reflectance maps for machine vision”, Computer Vision and Image Understanding, vol. 57, no. 3 pp. 265–282, Mar. 1993.CrossRefGoogle Scholar
  18. [18]
    P. Tsai, M. Shah, “Shape from shading using linear approximation”, Technical Report, Univ. Central Florida, USA, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Goffredo G. Pieroni
    • 1
  • Gian Luca Foresti
    • 1
  • Vittorio Murino
    • 1
  1. 1.DIMI - Dept. of Mathematics and Computer ScienceUniversity of UdineUdineItaly

Personalised recommendations