Image Boundaries Detection: From Thresholding to Implicit Curve Evolution

The development of high dimensional large-scale imaging devices increases the need of fast, robust and accurate image segmentation methods. Due to its intrinsic advantages such as the ability to extract complex boundaries, while handling topological changes automatically, the level set method (LSM) has been widely used in boundaries detection. Nevertheless, their computational complexity limits their use for real time systems. Furthermore, most of the LSMs share the limit of leading very often to a local minimum, while the effectiveness of many computer vision applications depends on the whole image boundaries. In this paper, using the image thresholding and the implicit curve evolution frameworks, we design a novel boundaries detection model which handles the above related drawbacks of the LSMs. In order to accelerate the method using the graphics processing units, we use the explicit and highly parallelizable lattice Boltzmann method to solve the level set equation. The introduced algorithm is fast and achieves global image segmentation in a spectacular manner. Experimental results on various kinds of images demonstrate the effectiveness and the efficiency of the proposed method.