图像的局部特征尺度在进行特征提取和构造尺度不变量时非常重要。提出了一种基于Gabor小波的局部特征尺度提取方法，该方法利用视皮层简单细胞的2维Gabor函数模型，构造了一个Gabor尺度空间核函数，利用该核函数计算图像的Gabor尺度空间分解，并在尺度空间中搜索局部极大值作为特征点的固有尺度。实验结果表明，该方法可在不同对比度条件下有效地提取各类特征的局部尺度，并且相比高斯拉普拉斯(LoG)方法有更好的适应性和可靠性。

Local characteristic scale of images plays an important role in feature extraction and local scale invariant construction. This paper proposed a local characteristic scale selection method based on Gabor wavelets, in which a Gabor scale-space kernel was formed using the 2D Gabor function model of simple cortical cells. A Gabor scale-space representation of the image was calculated based on the kernel first, then the maximum over scales of the feature point was detected and the scale corresponding to the maximum was selected as the characteristic scale. Experiments results show that the proposed method can select the characteristic scale effectively for different types of features in different conditions. Comparison with LoG method shows that the proposed method has better accuracy, applicability and reliability.