一种基于频域特征的仿射不变目标识别方法

An Affine-Invariant Objects Recognition Method Employing Features in Frequency Domain

针对低信噪比图像中仿射不变目标的识别问题,提出了一种基于图像频域特征的识别方法.通过分析空频域仿射变换之间的关系,采取对边缘图像的傅氏频谱进行伪对数采样的特征提取方法,较好地提取了中低频特征,避免了光照变化带来的不利影响,抑制了高频噪声;使用神经网络进行识别,有效地提取了目标的仿射不变特征,识别速度快.实验仿真了识别率随噪声强度的变化情况.结果显示,在信噪比低于-20dB时,识别率仍然高于90%,识别快速、稳定,优于基于Gabor滤波的识别方法.

To recognize images under various view-angle in noised background is one of the most important and challenging problems that need be resolved. An approach using affine invariant features in frequency domain for object recognition in low signal noise ratio （SNR） images is presented. The relationship of affine transforms between spatial domain and frequency domain is analyzed. That is, the effect of affine transformation on the spectrum is almost the same as the affine transform on the object in spatial domain except for two major differences： Firstly, the spectrum is inversely scaled and slanted; Secondly, shape translations parallel to the image plane do not affect the spectrum. In the preprocessing procedure, noise is reduced by convoluting the input image with a low-pass filter. Then the spectral signatures at low-to-median frequency are extracted using pseudo-log sampling, which is useful to reduce the influences of both view-point and illumination variation and to further suppress high frequency noises. A neural network is trained with these features to extract affine invariant features and to recognize objects at different poses. Comparing the proposed method with other two Gabor-based methods, experimental results show that more than 90% images can be recognized correctly even when SNR drops bellow -20 dB and this approach is much better than the Gabor-based methods in low SNR images.