Depth Estimation of a 2D Scene Using Scale Variation of Texture Patterns

Abstract

In this paper, we proposed a new framework to perceive the relative depth information from a 2D scene using scale variation of texture image. To extract feature vectors from textured patterns, higher order local autocorrelation functions are utilized at each scale step. The hierarchical linear discriminant analysis is employed to classify the scale rate of the feature vector, which can be divided into subspaces by recursively grouping the overlapped classes. In the experiment, relative depth perception of 2D natural scenes is performed on the proposed method and it is expected to play an important role in natural scene analysis.