A novel segmentation algorithm for noisy jacquard images based on finite element technique

Automatic pattern segmentation of jacquard images plays an important role in jacquard pattern analysis. This paper deals with the problem of low accuracy in segmentation of jacquard images under noisy environment A novel iterative relaxation algorithm based on Mumford-Shah model was proposed. In this algorithm, the Mumford-Shah model was approximated in the sense of Γ-convergence by a sequence of discrete models defined on finite element spaces of adaptive triangulation. During each iteration, an adjustment procedure for the triangulation was enforced to characterize the essential contour structure of a jacquard pattern. A quasi-Newton algorithm was applied to find the absolute minimum of the discrete model at the current iteration. Experimental results on synthetic and jacquard images have shown the effectiveness and robustness of the algorithm.