Sobolev Gradient and Segmentation of Vector Valued Texture ImagesMS22

In this paper, we propose a method for minimization of segmentation model for vector-valued texture images. The texture in the image will be smoothed by using L0 gradient norm and then the vector valued image segmentation model will be minimized through sobolev gradient for fast convergence. The better performance of the method will observed from the experimental results. Results of the proposed method are compared with $L^2$ gradients.

This presentation is part of Minisymposium “MS22 - Mapping problems in imaging, graphics and vision (3 parts)
organized by: Ronald Lui (Chinese University of Hong Kong) , Ke Chen (University of Liverpool) .

Fahim Ullah (University of Engineering and Technology Peshawar)