Level set evolution with sparsity constraint for object extraction

Abstract

Researchers utilised various types of information in active contour models to define new energy functionals for image segmentation. These models aim to extract all potential objects from the background, but non-target objects and noise are also obtained. In this study, the authors aim to extract target objects with sparse representation method. The original indicator function (a binary function) with respect to the level set function is used to represent the foreground (value is 1) and background(value is 0). From another point of view, an indicator function can be represented by linear combination of a set of the basis function. Firstly, by a label operator for the indicator function in each iteration, every connected area is represented by a basis function. Secondly, the linear combination of these basis functions is used to represent objects. Finally, through the sparsity constraint of coefficients of basis functions, the object extraction is viewed as a sparse representation problem. Meanwhile, a corresponding improved orthogonal matching pursuit algorithm is designed to obtain the ideal results. Experiments demonstrate that the proposed method has superior performance in object extraction compared with state-of-the-art active contour models. Furthermore, the proposed method also increases the flexibility of applications.