Performing Gabor Transform on Images with MATLAB Implementation

In this article, we explore how to perform Gabor transform on images using MATLAB to extract image eigenvalues. First, we need to understand what Gabor transform is and its significance in image processing. The Gabor transform is a mathematical tool commonly used in image processing that can capture texture detail information in images. The implementation typically involves creating a Gabor filter bank with multiple orientations and scales using MATLAB's gaborFilterBank function, which generates complex-valued filters combining Gaussian and sinusoidal components.

Next, we demonstrate how to apply Gabor transform functions in MATLAB to process images. This process involves convolving the input image with each Gabor filter in the bank using the imfilter function, resulting in a set of complex responses. The magnitude responses are then computed to obtain feature values that describe the texture characteristics of the image. The code implementation typically includes parameter configuration for wavelength (lambda), orientation angles (theta), and spatial aspect ratio (gamma).

Finally, we discuss how to analyze these eigenvalues to gain deeper insights. This involves feature vector construction by concatenating statistical measures (mean, standard deviation) computed from the magnitude responses across different filter configurations. The resulting feature vectors can be used for various applications such as texture classification, object recognition, and image segmentation using machine learning algorithms.