Abstract
An object recognition algorithm is put forward based on statistical character of contourlet transform and multi-object wavelet neural network (MWNN). A contourlet-based feature extraction method is proposed, which forms the feature vector taking advantage of the statistical attribution in each sub-band of contourlet transform. And then the extracted features are weighted according to their dispersion degree of data. WNN is used as classifier, which combines the extraction local singularity of wavelet transform and adaptive of artificial neural network. With the application in an aircraft recognition system, the experimental data showed the efficiency of this algorithm for automation target recognition.
Keywords: Automatic target recpgnition, Wavelet neural network, Contourlet transform, Feature extraction.
This work was supported by United Project of Yang-Zi Delta Integration under grant number 2005E60007.
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Mei, X., Xia, L., Li, J. (2007). A Contourlet-Based Method for Wavelet Neural Network Automatic Target Recognition. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_106
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DOI: https://doi.org/10.1007/978-3-540-72393-6_106
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