Painterly Rendering by Using K-mean Segmentation

Painterly Rendering using Image Salience

The contribution of this paper is a novel nonphotorealistic rendering (NPR) technique, capable of producing an artificial ‘hand-painted’ effect on 2D images, such as photographs. Our method requires no user interaction, and makes use of image salience and gradient information to determine the implicit ordering and attributes of individual brush strokes. The benefits of our technique are complet...

Painterly Rendering Using Human Vision

Painterly rendering has been linked to computer vision, but we propose to link it to human vision because perception and painting are two processes that are interwoven. Recent progress in developing computational models allows to establish this link. We show that completely automatic rendering can be obtained by applying four image representations in the visual system: (1) colour constancy can ...

Salience-adaptive Painterly Rendering Using Genetic Search

We present a new non-photorealistic rendering (NPR) algorithm for rendering photographs in an impasto painterly style. We observe that most existing image-based NPR algorithms operate in a spatially local manner, typically as non-linear image filters seeking to preserve edges and other high-frequency content. By contrast, we argue that figurative artworks are salience maps, and develop a novel ...

Flow-Centric Painterly Rendering

We present a painterly rendering technique that emphasizes smooth line flows to describe important shapes in the image, as well as contributing in an essential part to the final stylistic artwork. Our method uses the smoothed edge tangent flow (ETF) [20] for stroke pathing, and provides an artist-friendly way to control the general look and feel of the image using familiar brush stroke properti...

Reverse Painterly Rendering