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The Study of Image Feature Extraction Based on Independent Component Analysis

机译:基于独立分量分析的图像特征提取研究

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This paper mainly focuses on the independent component analysis (ICA) and image recognition algorithm research, presents the supersaturated algorithm which mixed signal dimension can be smaller than the separation independent component dimension, ensure the reasonable precision. In the actual application it means that using a small number of signal acquisition device can get the character of the object, which largely reduces the processing cost, extends the application domains of ICA.
机译:本文主要研究独立分量分析(ICA)和图像识别算法的研究,提出了混合信号维数可以小于分离独立分量维数,保证合理精度的过饱和算法。在实际应用中,这意味着使用少量的信号采集设备就可以得到物体的特征,从而大大降低了处理成本,扩展了ICA的应用领域。

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