LBP-guided active contours

This paper investigates novel LBP-guided active contour approaches to texture segmentation. The Local Binary Pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for a variety of applications. In this light, two LBP-guided active contours have been formulated, namely the scalar-LBP Active Contour (s-LAC) and the vector-LBP Active Contour (v-LAC). These active contours combine the advantages of both the LBP texture representation and the vector-valued Active Contour Without Edges model, and result in high quality texture segmentation. s-LAC avoids the iterative calculation of active contour equation terms derived from textural feature vectors and enables efficient, high quality texture segmentation. vLAC evolves utilizing regional information encoded by means of LBP feature vectors. It involves more complex computations than s-LAC but it can achieve higher segmentation quality. The computational cost involved in the application of v-LAC can be reduced if it is preceded by the application of s-LAC. The experimental evaluation of the proposed approaches demonstrates their segmentation performance on a variety of standard images of natural textures and scenes.