Color-Texture Image Segmentation Algorithms based on Hypercomplex Gabor Analysis

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. In this paper, we first extend the well-known Gabor filters to color images using a specific form of hypercomplex numbers known as quaternions. Based on this extension this paper presents the use of these new quaternionic Gabor filters in colour texture image segmentation. Different types of segmentation algorithms based on these quaternionic Gabor filters have been followed to carry out texture segmentation. Both linear and circular quaternionic Gabor filters are analyzed in this aspect and how circular filters are better in comparison to linear filters is also analyzed.