|Modeling Smoothly Varying Texture|
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.
People Involved: Jason Chang, John W. Fisher III
|Original Image||Contrast Energy||Bias (Average Intensity||Orientation||Scale (Brighter is coarser)||Intrinsic Texture Image|
|Image||Segmentation||Shading||Reflectance||Shape View 1||Shape View 2|
The Entire Process
Joint segmentation, radiometric response estimation, shading estimation, and shape from shading. The top left image is the current segmentation. The top middle image is the labels assigned. The top right image represents the likelihood of any pixel belonging to a specific region label. The bottom images represent the estimated shape of the zebra based on the current segmentation.