“Smelling” chemicals and bacteria by using structural color: the photonic nose is a novel platform for the identification of volatile chemicals based on color changes of porous Bragg stack arrays with potential for applications in chemical sensing and bacteria identification.

No abstract

This article presents an algorithm for learning hatching styles from line drawings. An artist draws a single hatching illustration of a 3D object. Her strokes are analyzed to extract the following per-pixel properties: hatching level (hatching, cross-hatching, or no strokes), stroke orientation, spacing, intensity, length, and thickness. A mapping is learned from input geometric, contextual, and shading features of the 3D object to these hatching properties, using classification, regression, and clustering techniques. Then, a new illustration can be generated in the artist's style, as follows. First, given a new view of a 3D object, the learned mapping is applied to synthesize target stroke properties for each pixel. A new illustration is then generated by synthesizing hatching strokes according to the target properties.

We present a synthesis technique that can automatically generate stroke patterns based on a user-specified reference pattern.