Segmentation and Shading Estimation in Slowly Changing Textured Images

We present a novel representation for modeling textured regions subject to smooth variations in orientation and scale. Utilizing the steerable pyramid of Simoncelli and Freeman as a basis, we decompose textured regions of natural images into explicit local attributes of contrast, bias, scale, and orientation. Additionally, we impose smoothness on these attributes via Markov random fields. The combination allows for demonstrable improvements in common scene analysis applications including unsupervised segmentation, reflectance and shading estimation, and estimation of the radiometric response function from a single image.

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Jason Chang, John W. Fisher III
• Keywords: Computer Vision, ICCV 2009, Pattern Recognition