Object boundary extraction using Active B-Snake Model

A new deformable model called active b-snake model (ABM) is presented for object boundary extraction. First, an affine-invariant landmark point assignment strategy is proposed to avoid manually assign landmarks. Second, an adaptive control point insertion algorithm is used to enhance the flexibility of B-Snake to describe complex shape. Third, for modeling the shape distribution and appearance characteristics of landmark points in the training samples, a statistical framework is embedded into ABM. Finally, the minimum mean square error (MMSE) approach with control point insertion provides fine deformation to the desired object boundaries. Experimental results show that ABM can be used to achieve more accurate object extraction.