Image matching belongs to constrained optimization problems. Whether the system would converge to the global optimum is still an open problem. To produce smooth and detailed disparity map, three assumptions--uniqueness, continuity and ordering--are generally adopted. However, if a point appears in one image but it is occluded in the other one, above assumptions are invalid. While the unwanted smoothing occurs in the resultant disparity map, a surface with high intensity variation extends into neighboring surfaces with less variation across occluding boundaries. This fact creates the phenomena of so-called fattening and shrinkage of a surface. Therefore, when considering the definition of criteria for an optimal match in one matching algorithm, some other matching constraints must be imposed based on the internal image information, to ensure the global accurate matching results accompanied with occlusion detection. A sparse-to-dense matching approach is presented, which utilizes the energy map to gain sparse but high confidence disparity map ,the phase matching to interpolate the disparity results and the optimization theories to avoid local extrema and detect occlusion areas.The experimental results demonstrate the validity of the proposed approach.