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Enhanced Decoupled Active Contour Using Structural and Textural Variation Energy Functionals

Active contours are a popular approach for object segmentation that uses an energy minimizing spline to extract an object's boundary. Nonparametric approaches can be computationally complex, whereas parametric approaches can be impacted by parameter sensitivity. A decoupled active contour (DAC) overcomes these problems by decoupling the external and internal energies and optimizing them separately. However a drawback of this approach is its reliance on the edge gradient as the external energy. This can lead to poor convergence toward the object boundary in the presence of weak object and strong background edges. To overcome these issues with convergence, a novel approach is proposed that takes advantage of a sparse texture model, which explicitly considers texture for boundary detection. The approach then defines the external energy as a weighted combination of textural and structural variation maps and feeds it into a multifunctional hidden Markov model for more robust object boundary detection. The enhanced DAC (EDAC) is qualitatively and visually analyzed on two natural image data sets as well as Brodatz images. The results demonstrate that EDAC effectively combines texture and structural information to extract the object boundary without impact on computation time and a reliance on color.