A pointillism style for the non-photorealistic display of augmented reality scenes

The ultimate goal of augmented reality is to provide the user with a view of the surroundings enriched by virtual objects. Practically all augmented reality systems rely on standard real-time rendering methods for generating the images of virtual scene elements. Although such conventional computer graphics algorithms are fast, they often fail to produce sufficiently realistic renderings. The use of simple lighting and shading methods, as well as the lack of knowledge about actual lighting conditions in the real surroundings, cause virtual objects to appear artificial. We have recently proposed a novel approach for generating augmented reality images. Our method is based on the idea of applying stylization techniques for reducing the visual realism of both the camera image and the virtual graphical objects. Special non-photorealistic image filters are applied to the camera video stream. The virtual scene elements are rendered using non-photorealistic rendering methods. Since both the camera image and the virtual objects are stylized in a corresponding way, they appear very similar. As a result, graphical objects can become indistinguishable from the real surroundings. Here, we present a new method for the stylization of augmented reality images. This approach generates a painterly "brush stroke" rendering. The resulting stylized augmented reality video frames look similar to paintings created in the "pointillism" style. We describe the implementation of the camera image filter and the non-photorealistic renderer for virtual objects. These components have been newly designed or adapted for this purpose. They are fast enough for generating augmented reality images in real-time and are customizable. The results obtained using our approach are very promising and show that it improves immersion in augmented reality

Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

Image preprocessing for artistic robotic painting

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot

The Evaluation of Stylized Facial Expressions

Stylized rendering aims to abstract information in an image making it useful not only for artistic but also for visualization purposes. Recent advances in computer graphics techniques have made it possible to render many varieties of stylized imagery efficiently. So far, however, few attempts have been made to characterize the perceptual impact and effectiveness of stylization. In this paper, we report several experiments that evaluate three different stylization techniques in the context of dynamic facial expressions. Going beyond the usual questionnaire approach, the experiments compare the techniques according to several criteria ranging from introspective measures (subjective preference) to task-dependent measures (recognizability, intensity). Our results shed light on how stylization of image contents affects the perception and subjective evaluation of facial expressions

ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos

postprin

Video-Based Stylized Rendering using Frame Difference

In this paper, we suggest video based stylized rendering using frame difference. Stylized rendering using video frame has a temporal problem that occurs a difference between the previous and current frame. To reduce the temporal problem, we generate reference maps using temporal frame difference in correction and rendering steps. A correction method using reference maps can be reduced flickering effect caused by frame difference between the previous and current frame. We use a background map, an average map, and a quadtree-based summed area table as reference maps. Among these reference maps, the method using quadtree based summed area table can completely remove a flickering and popping effect. Also, a post-blurring method using bilateral filtering can be represented smooth, stylized rendering by removing unnecessary noise. Suggested stylized rendering system can be used in various fields such as visual art, advertisement, game and movie for stylized image contents generation

A Framework for Creativity Enhancement of Painterly Rendering

東京電機大学201

Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information