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Proceedings Paper

Feature based recognition of submerged objects in holographic imagery
Author(s): Christopher R. Ratto; Nathaniel Beagley; Kevin C. Baldwin; Kara R. Shipley; Wayne I. Sternberger
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Paper Abstract

The ability to autonomously sense and characterize underwater objects in situ is desirable in applications of unmanned underwater vehicles (UUVs). In this work, underwater object recognition was explored using a digital holographic system. Two experiments were performed in which several objects of varying size, shape, and material were submerged in a 43,000 gallon test tank. Holograms were collected from each object at multiple distances and orientations, with the imager located either outside the tank (looking through a porthole) or submerged (looking downward). The resultant imagery from these holograms was preprocessed to improve dynamic range, mitigate speckle, and segment out the image of the object. A collection of feature descriptors were then extracted from the imagery to characterize various object properties (e.g., shape, reflectivity, texture). The features extracted from images of multiple objects, collected at different imaging geometries, were then used to train statistical models for object recognition tasks. The resulting classification models were used to perform object classification as well as estimation of various parameters of the imaging geometry. This information can then be used to inform the design of autonomous sensing algorithms for UUVs employing holographic imagers.

Paper Details

Date Published: 29 May 2014
PDF: 14 pages
Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 907205 (29 May 2014); doi: 10.1117/12.2049742
Show Author Affiliations
Christopher R. Ratto, Johns Hopkins Univ. Applied Physics Lab. (United States)
Nathaniel Beagley, Johns Hopkins Univ. Applied Physics Lab. (United States)
Kevin C. Baldwin, Johns Hopkins Univ. Applied Physics Lab. (United States)
Kara R. Shipley, Johns Hopkins Univ. Applied Physics Lab. (United States)
Wayne I. Sternberger, Johns Hopkins Univ. Applied Physics Lab. (United States)


Published in SPIE Proceedings Vol. 9072:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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