基于高斯-埃米特矩和ART神经网络的运动目标识别

Youfu Wu, Jing Wu
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引用次数: 7

摘要

矩广泛应用于模式识别、图像处理、计算机视觉和多分辨率分析等领域。本文首先打印出高斯-埃尔米特矩,并提出了一种基于高斯-埃尔米特矩提取目标特征的新方法。接下来,为了训练ART神经网络,将矩特征作为ART的参数输入ART;从而实现了对运动物体的分类器识别。实验结果表明,该方法具有良好的性能。
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Recognizing Moving Objects Based on Gaussian-Hermite Moments and ART Neural Networks
Moments are widely used in pattern recognition, image processing, and computer vision and multi resolution analysis. In this paper, we first printout Gaussian-Hermite moments, and propose a new method to extract the object’s features based on Gaussian-Hermite moments. Following, for training ART neural network, the moment features were inputted to ART as its parameters; so that, a classifier was realized for recognizing the moving objects. The experiment results are reported also, which show the good performance of our method.
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