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Proceedings Paper

Recognizing elongated objects using invariant surface features and matched filters
Author(s): Qian Huang; George C. Stockman
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Paper Abstract

Many biological objects are elongated. This research addresses the issue of recognizing elongated objects from both 2D intensity images and 3D volumes. A mathematical model, called tube model, is developed for this class of objects and is effectively utilized in two stages of recognition. The explicit relationships between geometrical surface features and the object model parameters are quantitatively exploited to automatically locate seeds for recognition. Invariant surface features are used to constrain or hypothesize the objects of interest. The verification of a hypothesis is performed by correlating a matched filter, dynamically generated based on the hypothesis, with the sensor data. The tubes identified in such a local recognition process serve as the seeds from which the global recognition process is initiated. Each seed is swept along the trajectory where the best-fit is found. A smooth sweep is controlled by a set of adaptive constraints computed dynamically from an on-line sweeping history. We apply the proposed method to real world data from different application domains. Experimental results are presented and discussed.

Paper Details

Date Published: 23 June 1993
PDF: 12 pages
Proc. SPIE 2035, Mathematical Methods in Medical Imaging II, (23 June 1993); doi: 10.1117/12.146596
Show Author Affiliations
Qian Huang, Michigan State Univ. (United States)
George C. Stockman, Michigan State Univ. (United States)

Published in SPIE Proceedings Vol. 2035:
Mathematical Methods in Medical Imaging II
Joseph N. Wilson; David C. Wilson, Editor(s)

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