Artistic Content Representation and Modelling based on Visual Style Features

This thesis aims to understand visual style in the context of computer science, using traditionally intangible artistic properties to enhance existing content manipulation algorithms and develop new content creation methods. The developed algorithms can be used to apply extracted properties to other drawings automatically; transfer a selected style; categorise images based upon perceived style; build 3D models using style features from concept artwork; and other style-based actions that change our perception of an object without changing our ability to recognise it. The research in this thesis aims to provide the style manipulation abilities that are missing from modern digital art creation pipelines

Visual Comput (2005) 21: 1–10 DOI 10.1007/s00371-005-0304-4 ORIGINAL ARTICLE

In reverse engineering, curve reconstruction, in which attempts are made to reconstruct a piece of a curve in NURBS CE a form from a piece of a planar strip-shaped point cloud, plays an important role in surface reconstruction [5, 15]. Many well-studied methods have been proposed recently. Pottmann and Randrup [10] proposed discretizing the involved plane into a binary image first and calculating its medial axis by a conventional imagethinning algorithm. A parametric curve is then fitted to points lying in the medial axis. Another method, presented by Goshtasby [3], computes a radial basis function (RBF) surface based on a point cloud and discretizes the RBF surface into an image. The reconstructed curve from the point cloud can be computed by tracing the spine of the image. Obviously, the accuracy of resultant curves using the above two methods is dominated by the image resolution. Curve reconstruction based on an interval B-spline curve Abstract Curve reconstruction that generates a piece of centric curve from a piece of planar stripshaped point cloud is a fundamental problem in reverse engineering. In this paper, we present a new curvereconstruction algorithm based on an interval B-spline curve. The algorithm constructs a rectangle sequence approximating the point cloud using a new data clustering technique, which facilitates the determination of curve order implied in the shape of the point cloud. A quasicentric point sequence and two pieces of boundary point sequences are then computed, based on which a piece of interval B-spline curve representing the geometric shape of the point cloud is constructed. Its centric curve is the final reconstructed curve. The whole algorithm is intuitive, simple, and efficient, as demonstrated by experimental results