Image Segmentation via Convolution of a Level-Set Function with a Rigaut Kernel.

Image segmentation is a fundamental task in Computer Vision and there are numerous algorithms that have been successfully applied in various domains. There are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image.

Color Image Segmentation in a Quaternion Framework.

In this paper, we present a feature/detail preserving color image segmentation framework using Hamiltonian quaternions. First, we introduce a novel Quaternionic Gabor Filter (QGF) which can combine the color channels and the orientations in the image plane. Using the QGFs, we extract the local orientation information in the color images.