Automatic wood log segmentation using graph cuts

Segmenting foreground from background automatically is an active field of research. The graph cut approach is one of the promising methods to solve this problem. This approach requires that the weights of the graph are chosen optimally in order to obtain a good segmentation. We address this challenge focusing on the automatic segmentation of wood log images. We present a novel method based on density estimation to obtain information about both foreground and background. With this information the weights in the graph cut method can be set automatically. In order to validate our results, we use four different methods to set these weights. We show that of these approaches, our new method obtains the best results.