Gabor Wavelets in Image Processing
This work offers a domain-specific method for image processing that may be useful in scenarios with fast or precomputed Gabor transforms, but it appears incremental as it builds on existing wavelet techniques.
The paper tackles the problem of detecting interest points like edges, corners, and blobs in images by using Gabor wavelets as multiscale partial differential operators, and it compares performance to Haar wavelets and Gaussian derivatives, though no concrete numbers are provided.
This work shows the use of a two-dimensional Gabor wavelets in image processing. Convolution with such a two-dimensional wavelet can be separated into two series of one-dimensional ones. The key idea of this work is to utilize a Gabor wavelet as a multiscale partial differential operator of a given order. Gabor wavelets are used here to detect edges, corners and blobs. A performance of such an interest point detector is compared to detectors utilizing a Haar wavelet and a derivative of a Gaussian function. The proposed approach may be useful when a fast implementation of the Gabor transform is available or when the transform is already precomputed.