By-Example Synthesis of Vector Textures
This work addresses texture synthesis for graphics and design applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of synthesizing arbitrarily sized vector textures from a single raster exemplar by segmenting and clustering textons, computing neighborhood descriptors, and adjusting colors to match a gradient field, achieving results comparable to other methods using perceptual-based metrics.
We propose a new method for synthesizing an arbitrarily sized novel vector texture given a single raster exemplar. Our method first segments the exemplar to extract the primary textons, and then clusters them based on visual similarity. We then compute a descriptor to capture each texton's neighborhood which contains the inter-category relationships that are used at synthesis time. Next, we use a simple procedure to both extract and place the secondary textons behind the primary polygons. Finally, our method constructs a gradient field for the background which is defined by a set of data points and colors. The color of the secondary polygons are also adjusted to better match the gradient field. To compare our work with other methods, we use a wide range of perceptual-based metrics.