A new sparse feature-based patch for dense correspondence
This paper presents a new method to compute the dense correspondences between two images by using the sparse feature-based patches in an energy optimization framework. Many transformation and deformation cues such as color, scale and rotation should be considered when we finding dense correspondences between images. However, most existing methods only consider part of these transformations, which will introduce the uncorrect correspondence results. In terms of the property of the sparse feature and the principle that nearest sub-scenes and neighbors are much more similar, we design a new energy optimization to guide the dense matching process. Both transformation and deformation are considered in our energy optimization framework since we design the feature-based patches. Thus, our algorithm can match the complicated scenes and objects robustly. At last, a local refinement technique is proposed to solve the perturbation of the matched patches. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms.