This article describes the application of the adaptive resonance theory (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images independently of their position and orientation is required for automatic tracking and target recognition. Invariance is achieved by using different invariant feature spaces in combination with an unsupervised neural network. The performance of the neural network based classifier in conjunction with several types of invariant AAIR global features, such as the Fourier transform (FT) space, Zernike moments, central moments and polar transforms, are examined. The advantages of this approach are discussed. The ART 2-A distinguished itself with its speed and low number of training vectors. Although a large image data base would be necessary before this approach could be fully validated, the initial results are very promising.
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机译:本文介绍了自适应共振理论(ART 2-A)网络在自动航空图像识别(AAIR)问题中的应用。自动跟踪和目标识别需要对航空图像进行分类,而不必考虑其位置和方向。通过使用不同的不变特征空间与无监督神经网络相结合来实现不变性。结合几种类型的不变AAIR全局特征(例如傅立叶变换(FT)空间,Zernike矩,中心矩和极坐标变换),研究了基于神经网络的分类器的性能。讨论了这种方法的优点。 ART 2-A以其速度快和训练向量数量少而著称。尽管在完全验证此方法之前将需要较大的图像数据库,但初步结果还是很有希望的。
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