Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Applied Mathematics
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A level set algorithm for minimizing the Mumford-Shah functional in image processing
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Automatic pattern segmentation of jacquard images is a challenging task due to the complexity of the images. Active contour models have become popular for finding the contours of a pattern with a complex shape. However, these models have many limitations on the pattern segmentation of jacquard images in the presence of noise. In this paper, a robust algorithm based on the Mumford-Shah model is proposed for the segmentation of noisy jacquard images. We discretize the Mumford-Shah model on piecewise lin-ear finite element spaces to yield greater stability and higher accuracy. A novel iterative relaxation algo-rithm for the numerical solving of the discrete version of the Mumford-Shah model is presented. During each iteration, we first refine and reorganize an adaptive triangular mesh to characterize the essential contour structure of a pattern. Then, we apply the quasi-Newton algorithm to find the absolute minimum of the discrete version of the model at the current iteration. Experimental results on synthetic and jac-quard images have shown the effectiveness and robustness of the algorithm.