Robust estimation of texture flow via dense feature sampling

Abstract

Texture flow estimation is a valuable step in a variety of vision related tasks, including texture analysis, image segmentation, shape-from-texture and texture remapping. This paper describes a novel and effective technique to estimate texture flow in an image given a small example patch. The key idea consists of extracting a dense set of features from the example patch where discrete orientations are encapsulated into the feature vector such that rotation can be simulated as a linear shift of the vector. This dense feature space is then compressed by PCA and clustered using EM to produce a set of small set of principal features. Obtaining these principal features at varying image scales, we can compute the per-pixel scale and orientation likelihoods for the distorted texture. The final texture flow estimation is formulated as the MAP solution of a labeling Markov network which is solved using belief propagation. Experimental results on both synthetic and real images demonstrate good results even for highly distorted examples.