A Novel Method for Vectorization
This addresses the need for efficient and customizable vectorization in computer graphics and vision, particularly for artistic or low-information-loss applications, but it appears incremental as it builds on existing spline techniques.
The paper tackles the problem of vectorizing raster images by introducing an algorithm based on Catmull Rom spline fitting, achieving a balance between photo-realism and abstraction, with results that are fast, parallelizable, and aesthetically smoother than polygon-based methods.
Vectorization of images is a key concern uniting computer graphics and computer vision communities. In this paper we are presenting a novel idea for efficient, customizable vectorization of raster images, based on Catmull Rom spline fitting. The algorithm maintains a good balance between photo-realism and photo abstraction, and hence is applicable to applications with artistic concerns or applications where less information loss is crucial. The resulting algorithm is fast, parallelizable and can satisfy general soft realtime requirements. Moreover, the smoothness of the vectorized images aesthetically outperforms outputs of many polygon-based methods