Shape from shading based on needle map and cellular automata
This paper presents a method for computing depth from a single grayscale image in two steps. Firstly, surface normals are parallelly and gradually adjusted by a procedure which includes three constraints: the smooth constraint ensures the recovered normals are smooth and integrable, the intensity gradient constraint ensures the recovered normals are consistent with the image gradient field, and the intensity constraint guarantees the recovered intensity is equal to the input image. Unlike their usage in global methods, those constraints are separately used in local area in our method. Secondly, the surface is recovered from needle map using a two-dimensional cellular automata system. An experimental assessment is provided for our methods on both real world images and synthetic images with known ground truth. The experiment results demonstrate this approach is practicable and has better precision than traditional methods.
shape from shading needle map cellular automata computer vision quadric smooth