Texture Analysis of a Color Image Using Traditional and Circular Gabor Filters

N. Sasikala, G. Shruthi

Color image, Gabor filters, Traditional Gabor function.

According to computer vision, segmentation is defined as the process of partitioning a digital image into multiple segments, where multiple segments are sets of pixels, in other words super pixels. Main objective of segmentation is to change and, or simplify the representation of a digital image into something that is much more significant and easier to analyze. Objects and boundaries like lines, curves, etc. in images can be normally located by using image segmentation. More accurately, the process of assigning a tag to every pixel in an image such that pixels with the same label share specific visual characteristics is known as image segmentation. The outcome of image segmentation is a set of surface ( especially of a curving form ) extracted from the image, a set of segments that as a group cover the entire image. In a segment every pixels are similar with regard to computed property or some characteristic, such as intensity, texture, or color. A Gabor filter is a linear filter used for edge detection in image processing which is named after Dennis Gabor. Gabor filter frequency and orientation representations are similar to those of human visual system, for texture representation and discrimination it has been found to be remarkably appropriate. Gabor filter is a powerful tool in texture analysis. Traditional Gabor function ( TGF ) represents a Gaussian function modulated with the help of an oriented complex sinusoidal signal.

10.22161/ijaers/3.9.17