Functional data approximation on bounded domains using polygonal finite elements. Cao Juan, Xiao Yanyang, Chen Zhonggui, Wang Wenping, and Bajaj Chandrajit.A finite element framework based on bivariate simplex splines on triangle configurations. Cao Juan, Chen Zhonggui, Wei Xiaodong, and Zhang Yongjie Jessica.A computational approach to edge detection. Vectorization of line drawings via polyvector fields. Bessmeltsev Mikhail and Solomon Justin.Vectorization of Raster Images Using B-Spline Surfaces. Our vectorization representation also facilitates a variety of editing operations performed directly over vector images. In particular, our method is able to model undetected features and subtle or complicated color variations in-between features, which the previous methods cannot handle efficiently. Experiments and comparisons show that our framework outperforms the existing state-of-the-art methods in providing more faithful reconstruction results.
#IMAGE VECTORIZER BEFORE AND AFTER UPDATE#
A variational knot mesh generation method is designed to adaptively introduce knots and update their connectivity to satisfy the local reconstruction quality. By using collinear knots at feature lines, both smooth and discontinuous color variations can be efficiently modeled by the same set of quadratic TCB-splines. It iteratively optimizes color and position of control points and updates the knot meshes. The proposed framework first detects sharp curvilinear features in the image and constructs knot meshes based on the detected feature lines. Based on this new representation, an automatic raster image vectorization paradigm is proposed. This article presents triangular configuration B-spline (referred to as TCB-spline)-based vector graphics for raster image vectorization. Vector image representation methods that can faithfully reconstruct objects and color variations in a raster image are desired in many practical applications.
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