The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.
© 2005 Chinese Optics Letters
Shijun Zhang, Zhongliang Jing, and Jianxun Li, "Morphological self-organizing feature map neural network with applications to automatic target recognition," Chin. Opt. Lett. 3, 12-15 (2005)