Gabor Function
Mathematical tool used in image processing and signal analysis, known for its ability to localize information in both the spatial and frequency domains simultaneously.
The Gabor function, or Gabor filter, is particularly valued in image processing and computer vision for texture analysis, feature extraction, and edge detection. It combines Gaussian functions and complex exponentials, enabling it to capture both spatial and frequency information with high precision. This dual localization makes Gabor filters ideal for analyzing non-stationary signals, such as textures in images, where frequency components vary across space. Their application extends to fields like biometrics, where they aid in fingerprint and iris recognition by highlighting specific patterns and features in image data.
The Gabor function was first introduced by Dennis Gabor in 1946 in his seminal paper "Theory of communication." It gained prominence in the fields of signal processing and image analysis in the 1980s and 1990s, particularly as computational power and digital image processing techniques advanced.
Dennis Gabor, a Hungarian-British physicist and electrical engineer, is the primary figure associated with the development of the Gabor function. His work laid the groundwork for the theory of communication and holography, earning him the Nobel Prize in Physics in 1971. Subsequent advancements and applications in image processing were driven by researchers like John Daugman, who utilized Gabor filters in iris recognition algorithms.