Gabor Wavelets in Image Processing

TL;DR

This paper explores the application of two-dimensional Gabor wavelets in image processing, demonstrating their effectiveness in detecting edges, corners, and blobs, and comparing their performance to other wavelet-based detectors.

Contribution

It introduces a novel use of Gabor wavelets as multiscale differential operators for feature detection in images, highlighting their advantages and implementation considerations.

Findings

Gabor wavelets effectively detect image features like edges, corners, and blobs.

Performance comparison shows Gabor wavelets are competitive with Haar and Gaussian derivatives.

The approach benefits from fast Gabor transform implementations.

Abstract

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.