Fuzzy c-means clustering based segmentation and the filtering method for discontinuous ESPI fringe patterns.

Abstract

The filtering of ESPI fringe patterns with both noise and discontinuity is a challenging problem raised in recent years. Discontinuity-detectable and discontinuity-aware processing techniques are demanded. In this paper, a fuzzy c-means (FCM) clustering based fringe segmentation method is proposed. By applying the FCM clustering method to the estimated fringe orientation, we segment the discontinuous ESPI fringe patterns into continuous segments, thus the discontinuous region is identified and a discontinuous region mask is generated. Then, the discontinuous region mask is introduced into the controlling speed function, and an adaptive shape-preserving oriented partial differential equation (OPDE) model is proposed for discontinuous ESPI fringe patterns denoising. According to our method, the discontinuous regions are effectively found and with the proposed adaptive shape-preserving OPDE, the noise is well eliminated, the shape of fringes and the discontinuity are well kept. The performance of our method is illustrated with three computer-simulated and one experimentally obtained discontinuous ESPI fringe patterns and comparison with related segmentation methods and OPDEs.