Abstract
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.
Chapter PDF
Keywords
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.
References
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. 2 463–467
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. 1 250–254
Campbell,.N.W., Thomas, B.T.: Segmentation of natural images using self organising feature maps. British Machine Vision Conference Proceedings. (1996) 223–232
Mohr, R., Triggs, B.: Projective geometry for image analysis. A tutorial given at ISPRS in Vienna. (1996)
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. (1995) 234–241
Schalkoff, R.: Pattern Recognition, Statistical, Structural and Neural Approaches. John Wiley & Sons Inc. (1992).
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.
Shapiro, L.S.: Affine analysis of image sequences. Cambridge University Press (1995).
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.
Sonka, M., et al.: Image Processing, Analysis and Machine Vision. Thomson Computer Press (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Voles, P., Smith, A.A.W., Teal, M.K. (2000). Nautical Scene Segmentation Using Variable Size Image Windows and Feature Space Reclustering. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_21
Download citation
DOI: https://doi.org/10.1007/3-540-45053-X_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67686-7
Online ISBN: 978-3-540-45053-5
eBook Packages: Springer Book Archive