Color and texture priors in active contours for model-based image segmentation

A solution to the problem of model-based image segmentation by integrating prior color and texture information into a parametric active contour model is proposed in this paper. The vector flow field technique is adopted as the computational scheme because of its large effective capture range. Color and texture priors are modeled in a uniform way to generate a potential field and its associated vector flow field for the repetitive application of the gradient vector field (GVF). The initialization of the snake is decided by studying the shape properties of the potential field. The shape information can be utilized to avoid unnecessary initializations caused by disturbing background similar color and texture features to the model object. The potential field is also valuable for determining the weights for integrating the color and texture based vector flow field and the edge based vector flow field. With the proper integration weights, the snake evolved on the combined vector flow field can bypass strong edges in the background and stop correctly on the object's boundary. Experimental results are shown on real scenes with complex backgrounds.