Gabor wavelet algorithm
The Gabor wavelet algorithm is a digital image processing technique used for various tasks such as image analysis, feature extraction, and texture analysis. It is based on the Gabor wavelet, which is a complex sinusoidal function modulated by a Gaussian window.\n\nThe algorithm applies a bank of Gabor filters to an image to extract different frequency and orientation components. Each Gabor filter is defined by its central frequency, bandwidth, and orientation. The filters are convolved with the image to produce a set of filtered images, each highlighting a specific component of the input image.\n\nThe Gabor wavelet algorithm is commonly used in computer vision applications such as face recognition, object detection, and fingerprint recognition. By extracting features at multiple scales and orientations, it provides a good representation of the local texture and structure of an image.\n\nOne of the advantages of the Gabor wavelet algorithm is its ability to capture both the spatial and frequency information of an image. It can detect small and elongated features and is robust to changes in lighting conditions and image noise. However, it can be computationally expensive due to the large number of Gabor filters involved.\n\nOverall, the Gabor wavelet algorithm is a powerful tool for image analysis and feature extraction, offering a rich representation of image content.