A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection").

Three-Dimensional Normal Facial Growth from Birth to the Age of 7 Years.

Background: With the advent of computer-assisted three-dimensional surface imaging and rapid data processing, oral and maxillofacial surgeons and orthodontists are enabled to analyze facial growth three dimensionally. Normative data, however, are still rare and inconsistent. The aim of the present study was to establish a valid reference system and to give normative data for facial growth.