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Proceedings Paper

Anomaly based vessel detection in visible and infrared images
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Paper Abstract

Detection of small vessels is a challenging task for navy, coast guard and port authority for security purposes. Vessel identification is more complex as compared to other object detection because of its variability in shapes, features and orientations. Current methods for vessel detection are primarily based on segmentation techniques which are not as efficient and also require different algorithms for visible and infrared images. In this paper, a new vessel detection technique is proposed employing anomaly detection. The input intensity image is first converted to feature space using difference of Gaussian filters. Then a detector filter in the form of Mahalanobis distance is applied to the feature points to detect anomalies whose characteristics are different from their surroundings. Anomalies are detected as bright spots in both visible and infrared image. The larger the gray value of the pixels the more anomalous they are to be. The detector output is then post-processed and a binary image is constructed where the boat edges with strong variance relative to the background are identified along with few outliers from the background. The resultant image is then clustered to identify the location of the vessel. The main contribution in this paper is developing an algorithm which can reliably detect small vessels in visible and infrared images. The proposed method is investigated using real-life vessel images and found to perform excellent in both visible and infrared images with the same system parameters.

Paper Details

Date Published: 3 February 2009
PDF: 6 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510B (3 February 2009); doi: 10.1117/12.805513
Show Author Affiliations
Mohammad Moinul Islam, Old Dominion Univ. (United States)
Mohammed Nazrul Islam, Old Dominion Univ. (United States)
K. Vijayan Asari, Old Dominion Univ. (United States)
Mohammad A. Karim, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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