A spatially constrained shifted asymmetric Laplace mixture model for the grayscale image segmentation

In this paper, the grayscale image segmentation problem is investigated and a new mixture model with shifted asymmetric Laplace distribution component is proposed. Instead of the conventional Gaussian model, the shifted asymmetric Laplace distribution model is adopted to model the pixels. The spatial constraint on neighboring pixels is introduced into the proposed shifted asymmetric Laplace mixture model, which makes the model be robust to noise and outliers of the images. The unknown model parameters are estimated via the expectation-maximization (EM) algorithm, which can guarantee convergence to a local minimum. The experimental verification is performed on both synthesized images and images of real chip to prove the effectiveness of our image segmentation method.