A fractal-based relaxation algorithm for shape from terrain image

摘要: We consider the problem of extracting surface shape from a single terrain image. Although fractal models play an important role in simulating models, various Shape-from-Shading (SFS) techniques that have been applied to this kind not coupled with prior. In paper, we define SFS imaging as fractal-regularized problem, and solve it using Maximum-A-Posterior (MAP) estimation. addition, also propose relaxation algorithm based on Landweber iteration order it. The optimum corresponding observed image does be convergent result. result can picked up during process number iterations specified by image-based estimation method proposed paper. Experimental results both simulated data real show our efficiently extract surfaces, is more accurate than some well-known algorithms, including Horn, Zheng-Chellappa, Tsai-Shah, Pentland linear, Lee-Rosenfeld methods.