Superpixel-based optimal seamline detection via graph cuts for panoramic images

Abstract

In this paper, we present a novel method for seamlessly mosaicking panoramic images based on superpixels in the graph cuts energy minimization framework. To effectively ensure that all seamlines are detected in the laterally continuous regions with the high image similarity and the low object dislocation, the energy functions adopted in graph cuts combine the pixel-level similarities of image characteristics, including intensity and gradient, and the texture complexity. Instead of finding the optimal solution of seamlines in overlap regions via graph cuts among the entire set of pixels, we find it among superpixels created from input images, which greatly improves the efficiency of the global graph cuts energy optimization because the number of elements in graph cuts dramatically decreases. Experimental results demonstrate that the superpixel-based method is capable of generating high-quality seamlines as the pixel-based method but greatly reduces the computation time.