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. v-LAC 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.