Stroke Based Painterly Rendering

International audienceMany traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This Chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed

Higher level techniques for the artistic rendering of images and video

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Exploring a Parameterized Portrait Painting Space

We overview our interdisciplinary work building parameterized knowledge domains and their authoring tools that allow for expression systems which move through a space of painterly portraiture. With new computational systems it is possible to conceptually dance, compose and paint in higher level conceptual spaces. We are interested in building art systems that support exploring these spaces and in particular report on our software-based artistic toolkit and resulting experiments using parameter spaces in face based new media portraiture. This system allows us to parameterize the open cognitive and vision-based methodology that human artists have intuitively evolved over centuries into a domain toolkit to explore aesthetic realizations and interdisciplinary questions about the act of portrait painting as well as the general creative process. These experiments and questions can be explored by traditional and new media artists, art historians, cognitive scientists and other scholars

Abstract art by shape classification

his paper shows that classifying shapes is a tool useful in nonphotorealistic rendering (NPR) from photographs. Our classifier inputs regions from an image segmentation hierarchy and outputs the "best” fitting simple shape such as a circle, square, or triangle. Other approaches to NPR have recognized the benefits of segmentation, but none have classified the shape of segments. By doing so, we can create artwork of a more abstract nature, emulating the style of modern artists such as Matisse and other artists who favored shape simplification in their artwork. The classifier chooses the shape that "best” represents the region. Since the classifier is trained by a user, the "best shape” has a subjective quality that can over-ride measurements such as minimum error and more importantly captures user preferences. Once trained, the system is fully automatic, although simple user interaction is also possible to allow for differences in individual tastes. A gallery of results shows how this classifier contributes to NPR from images by producing abstract artwork. INDEX TERM

Hierarchical Image Descriptions for Classification and Painting

The overall argument this thesis makes is that topological object structures captured within hierarchical image descriptions are invariant to depictive styles and offer a level of abstraction found in many modern abstract artworks. To show how object structures can be extracted from images, two hierarchical image descriptions are proposed. The first of these is inspired by perceptual organisation; whereas, the second is based on agglomerative clustering of image primitives. This thesis argues the benefits and drawbacks of each image description and empirically show why the second is more suitable in capturing object strucutures. The value of graph theory is demonstrated in extracting object structures, especially from the second type of image description. User interaction during the structure extraction process is also made possible via an image hierarchy editor. Two applications of object structures are studied in depth. On the computer vision side, the problem of object classification is investigated. In particular, this thesis shows that it is possible to classify objects regardless of their depictive styles. This classification problem is approached using a graph theoretic paradigm; by encoding object structures as feature vectors of fixed lengths, object classification can then be treated as a clustering problem in structural feature space and that actual clustering can be done using conventional machine learning techniques. The benefits of object structures in computer graphics are demonstrated from a Non-Photorealistic Rendering (NPR) point of view. In particular, it is shown that topological object structures deliver an appropriate degree of abstraction that often appears in well-known abstract artworks. Moreover, the value of shape simplification is demonstrated in the process of making abstract art. By integrating object structures and simple geometric shapes, it is shown that artworks produced in child-like paintings and from artists such as Wassily Kandinsky, Joan Miro and Henri Matisse can be synthesised and by doing so, the current gamut of NPR styles is extended. The whole process of making abstract art is built into a single piece of software with intuitive GUI.EThOS - Electronic Theses Online ServiceGBUnited Kingdo