Parametric active contour model using Gabor balloon energy for texture segmentation

Abstract

Active contour models (ACM) as deformable shape models are one of the popular methods in object detection and image segmentation. This article presents a robust texture-based segmentation method using parametric ACM. In the proposed method, the energy function of the parametric ACM is modified by adding texture-based balloon energy, so the accurate detection and segmentation of textured object in textured background would be achieved. In this study, texture features of contour, object, and background points are calculated by Gabor filter bank. Then, comparing the calculated texture features of contour points and target object obtains movement direction of the balloon, whereupon active contour curves are shrunk or expanded to make the contour fit to object boundaries. The comparison between our proposed segmentation method and the ACM based on the directional Walsh– Hadamard features, fast adaptive color snake model, and parametric texture model based on joint statistics of complex Wavelet coefficients, indicates that our method is more effective, accurate, and faster for texture image segmentation especially when the textures are irregular or texture direction of object and background is similar.