Abstract
This paper describes, using situational awareness and Genetic algorithm, a run-time optimization methodology of the Gabor wavelet parameters so that it produces a feature space for efficient object recognition. Gabor wavelet efficiently extracts the feature space of orientation selectivity, spatial frequency and spatial localization. Most previous object recognition approaches using Gabor wavelet do not include systematic optimization of the parameters for the Gabor kernel, even though the system performance might be much sensitive to the characteristics of the Gabor parameters. This paper explores efficient object recognition using adaptive Gabor wavelet based situational aware method. The superiority of the proposed system is shown using IT-Lab, FERET and Yale face database. We achieved encouraging experimental results.
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Jeon, I.J., Nam, M.Y., Rhee, P.K. (2005). Adaptive Gabor Wavelet for Efficient Object Recognition. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_41
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DOI: https://doi.org/10.1007/11552451_41
Publisher Name: Springer, Berlin, Heidelberg
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