Wavelet feature performance analysis for distortion-invariant target detection

Farid Ahmed; Mohammad A. Karim

doi:10.1117/12.213058

5 July 1995 Wavelet feature performance analysis for distortion-invariant target detection

Farid Ahmed, Mohammad A. Karim

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213058
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Farid Ahmed and Mohammad A. Karim "Wavelet feature performance analysis for distortion-invariant target detection", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213058

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