Nautical Scene Segmentation Using Variable Size Image Windows and Feature Space Reclustering

  • P. Voles
  • A. A. W. Smith
  • M. K. Teal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)


This paper describes the development of a system for the segmentation of small vessels and objects present in a maritime environment. The system assumes no a priori knowledge of the sea, but uses statistical analysis within variable size image windows to determine a characteristic vector that represents the current sea state. A space of characteristic vectors is searched and a main group of characteristic vectors and its centroid found automatically by using a new method of iterative reclustering. This method is an extension and improvement of the work described in [9]. A Mahalanobis distance measure from the centroid is calculated for each characteristic vector and is used to determine inhomogenities in the sea caused by the presence of a rigid object. The system has been tested using several input image sequences of static small objects such as buoys and small and large maritime vessels moving into and out of a harbour scene and the system successfully segmented these objects.


Feature Vector Feature Space Mahalanobis Distance Main Cluster Rigid Object 
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.
    Sanderson, J.G., Teal, M.K., Ellis, T.J.: Identification and Tracking in Maritime Scenes. IEE Int. Conference on Image Processing and its applications. (1997) Vol. 2 463–467CrossRefGoogle Scholar
  2. 2.
    Smith, A.A.W., Teal, M.K.: Identification and Tracking of Maritime Objects in Near-Infrared Image Sequences for Collision Avoidance. IEE 7th Int. Conference on Image Processing and its applications. (1999) Vol. 1 250–254CrossRefGoogle Scholar
  3. 3.
    Campbell,.N.W., Thomas, B.T.: Segmentation of natural images using self organising feature maps. British Machine Vision Conference Proceedings. (1996) 223–232Google Scholar
  4. 4.
    Mohr, R., Triggs, B.: Projective geometry for image analysis. A tutorial given at ISPRS in Vienna. (1996)Google Scholar
  5. 5.
    Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. (1995) 234–241Google Scholar
  6. 6.
    Schalkoff, R.: Pattern Recognition, Statistical, Structural and Neural Approaches. John Wiley & Sons Inc. (1992).Google Scholar
  7. 7.
    Sanderson, J.G., Teal, M.K., Ellis, T.J.: Characterisation of a Complex Maritime Scene using Fourier Space Analysis to Identify Small Craft. 7th IEE Int. Conference on Image Processing and its applications (1999) Vol. 2 803–807.CrossRefGoogle Scholar
  8. 8.
    Shapiro, L.S.: Affine analysis of image sequences. Cambridge University Press (1995).Google Scholar
  9. 9.
    Voles, P., Smith, A.A.W., Teal, M.K.: Segmentation of Nautical Scenes Using the Statistical Characteristics of Variable Size Image Windows. accepted for CVPRIP 2000.Google Scholar
  10. 10.
    Sonka, M., et al.: Image Processing, Analysis and Machine Vision. Thomson Computer Press (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • P. Voles
    • 1
  • A. A. W. Smith
    • 1
  • M. K. Teal
    • 1
  1. 1.Machine Vision GroupBournemouth UniversityUK

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