Recursive implementation of the Gaussian filter
Signal Processing
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Anisotropic Gauss Filtering
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Gabor Wavelet Networks for Object Representation
Mustererkennung 2000, 22. DAGM-Symposium
Comparison of Texture Features Based on Gabor Filters
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
IEEE Transactions on Signal Processing
Improving the SIFT descriptor with smooth derivative filters
Pattern Recognition Letters
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We describe a fast algorithm for Gabor filtering, specially designed for multi-scale image representations. Our proposal is based on three facts: first, Gabor functions can be decomposed in gaussian convolutions and complex multiplications which allows the replacement of Gabor filters by more efficient gaussian filters; second, isotropic gaussian filtering is implemented by separable 1D horizontal/vertical convolutions and permits a fast implementation of the non-separable zero-mean Gabor kernel; third, short FIR filters and the à trous algorithm are utilized to build a recursive multi-scale decomposition, which saves important computational resources. Our proposal reduces to about one half the number of operations with respect to state-of-the-art approaches.