Joint Estimation of Local Variance and Local Regularity for Texture Segmentation. Application to Multiphase Flow Characterization

Texture segmentation constitutes a task of utmost importance in statistical image processing. Focusing on the broad class of monofractal textures characterized by piecewise constancy of the statistics of their multiscale representations, recently shown to be versatile enough for real-world texture modeling, the present work renews this recurrent topic by proposing an original approach enrolling jointly scale-free and local variance descriptors into a convex, but non smooth, minimization strategy. The performance of the proposed joint approach are compared against disjoint strategies working independently on scale-free features and on local variance on synthetic piecewise monofractal textures. Performance are also compared for multiphase flow image characterization, a topic of crucial importance in geophysics as well as in industrial processes. Applied to large-size images (above two million pixels), the proposed approach is shown to significantly improve state-of-the-art strategies by permitting the detection of the smallest gas bubbles and by offering a better understanding of multiphase flow structures.