Abstract
The study of small target recognition in low SNR (Signal Noise Ratio) is the key problem about processing of forward-looking infrared (FLIR) images information. Eight features of objects based on IR radiation characteristics and wavelet-based are presented. These features are used to a radial basis function (RBF) network as input for learning and classification. The propose recognition algorithm is invariant to the translation, rotation, and scale channel of a shape. Experiments by real infrared images and noisy images are performed, and recognition results show that the method is very effective.
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Liu, J., Huang, X., Chen, Y., He, N. (2007). Target Recognition of FLIR Images on Radial Basis Function Neural Network. 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_92
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DOI: https://doi.org/10.1007/978-3-540-72393-6_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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