This article presents a framework for generating abstract art from photographs. The aesthetics of abstract art is largely attributed to its greater perceptual ambiguity than photographs. According to psychological theories [Berlyne 1971], the ambiguity tends to invoke moderate mental effort in the viewer for interpreting the underlying contents, and this process is usually accompanied by subtle aesthetic pleasure. We study this phenomenon through human experiments comparing the subjects' interpretations of abstract art and photographs, and quantitatively verify, the increased perceptual ambiguities in terms of recognition accuracy and response time. Based on the studies, we measure the level of perceptual ambiguity using entropy, as it measures uncertainty levels in information theory, and propose a painterly rendering method with interactive control of the ambiguity levels. Given an input photograph, we first segment it into regions corresponding to different objects and parts in an interactive manner and organize them into a hierarchical parse tree representation. Then we execute a painterly rendering process with image obscuring operators to transfer the photograph into an abstract painting style with increased perceptual ambiguities in both the scene and individual objects. Finally, using kernel density estimation and message-passing algorithms, we compute and control the ambiguity levels numerically to the desired levels, during which we may predict and control the viewer's perceptual path among the image contents by assigning different ambiguity levels to different objects. We have evaluated the rendering results using a second set of human experiments, and verified that they achieve similar abstract effects to original abstract paintings.

This paper presents a method to render artistic paper-cut of human portraits. Rendering paper-cut images from photographs can be considered as an inhomogeneous image binarization problem, to which ideal solutions should reproduce vivid image details with sparse cuts. Especially for portrait paper-cut, good artworks should capture impressive facial features. To achieve this goal, our approach integrates bottom-up and top-down cues to better determine the binary values. In the bottom-up phase, facial components are localized on the input photograph, and their draft binary versions are proposed. In the top-down phase, we use pre-collected representative paper-cut templates, with which we synthesize the final paper-cut image by matching them with the bottom-up proposals. Experimental results show that our approach can produce visually satisfactory results.

We present a semantics-driven approach for stroke-based painterly rendering, based on recent image parsing techniques [Tu et al. 2005; Tu and Zhu 2006] in computer vision. Image parsing integrates segmentation for regions, sketching for curves, and recognition for object categories. In an interactive manner, we decompose an input image into a hierarchy of its constituent components in a parse tree representation with occlusion relations among the nodes in the tree. To paint the image, we build a brush dictionary containing a large set (760) of brush examples of four shape/appearance categories, which are collected from professional artists, then we select appropriate brushes from the dictionary and place them on the canvas guided by the image semantics included in the parse tree, with each image component and layer painted in various styles. During this process, the scene and object categories also determine the color blending and shading strategies for inhomogeneous synthesis of image details. Compared with previous methods, this approach benefits from richer meaningful image semantic information, which leads to better simulation of painting techniques of artists using the high-quality brush dictionary. We have tested our approach on a large number (hundreds) of images and it produced satisfactory painterly effects.