Image-Based Chinese Seal Carving Generation with Non-parametric Sampling

By learning a training set of seal examples of the same style,the system can generate new seal carvings similar to this style through non-parametric sampling. Using K-nearest neighbor algorithm,stroke branches are interpolated to produce output strokes by linear combination.Each stroke branch is rendered by dragging a circular brush mask along a non-uniform B-spline curve interpolating the given sampling points.A new sampling method is introduced to extract feature points from the similar strokes of seal examples through constrained hierarchical subdivision.The algorithm is simple and comprehensible in geometry.Experimental results show that this approach can generate satisfying new articles of the desired styles.