Recognizing elongated objects using invariant surface features and matched filters

Qian Huang; George C. Stockman

doi:10.1117/12.146596

23 June 1993 Recognizing elongated objects using invariant surface features and matched filters

Qian Huang, George C. Stockman

Proceedings Volume 2035, Mathematical Methods in Medical Imaging II; (1993) https://doi.org/10.1117/12.146596
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

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.

Citation Download Citation

Qian Huang and George C. Stockman "Recognizing elongated objects using invariant surface features and matched filters", Proc. SPIE 2035, Mathematical Methods in Medical Imaging II, (23 June 1993); https://doi.org/10.1117/12.146596

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