Proceedings Paper
Wavelet feature performance analysis for distortion-invariant target detection| Format | Member Price | Non-Member Price |
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Paper Abstract
Wavelet feature performance for the detection and recognition of targets from noisy images is investigated. Training patterns with different noise contents are first employed to come up with a statistical model for the dissimilarity of the reference target and noisy inputs. This model is then analyzed with Daubechies wavelet filter with extremal phase and vanishing moment. Simulation results show the potential of wavelet features that can be used in the decision making subsystem to yield high discrimination between target and non-target.
Paper Details
Date Published: 5 July 1995
PDF: 6 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213058
Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)
PDF: 6 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213058
Show Author Affiliations
Farid Ahmed, Univ. of Dayton (United States)
Mohammad A. Karim, Univ. of Dayton (United States)
Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)
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