Stippling

Abstract: This paper presents an automatic method for producing stipple renderings from photographs, following the style of professional hedcut illustrations. For effective depiction of image features, we introduce a novel dot placement algorithm which adapts stipple dots to the local shapes. The core idea is to guide the dot placement along 'feature flow' extracted from the feature lines, resulting in a dot distribution that conforms to feature shapes. The sizes of dots are adaptively determined from the input image for proper tone representation. Experimental results show that such feature-guided stippling leads to the production of stylistic and feature-emphasizing dot illustrations.

Abstract: When people compare a computer-generated illustration to a hand-drawn illustration of the same object, they usually perceive differences. This seems to indicate that the two kinds of images follow different aesthetic principles. To explore and explain these differences, the authors compare texture stippling in hand-drawn and computer-generated illustrations, using image-processing analysis techniques.

Abstract: This paper presents a new fast, automatic method for structure-aware stippling. The core idea is to concentrate on structure preservation by using a priority-based scheme that treats extremal stipples first and preferentially assigns positive error to lighter stipples and negative error to darker stipples, emphasizing contrast. We also use a nonlinear spatial function to shrink or exaggerate errors and thus implicitly provide global adjustment of density. Our adjustment respects contrast and hence allows us to preserve structure even with very low stipple budgets. We also explore a variety of stylization effects, including screening and scratchboard, all within the unifying framework of stippling.

Abstract: We propose an adaptive version of Lloyd's optimization method that distributes points based on Voronoi diagrams. Our inspiration is the Linde-Buzo-Gray-Algorithm in vector quantization, which dynamically splits Voronoi cells until a desired number of representative vectors is reached. We reformulate this algorithm by splitting and merging Voronoi cells based on their size, greyscale level, or variance of an underlying input image. The proposed method automatically adapts to various constraints and, in contrast to previous work, requires no good initial point distribution or prior knowledge about the final number of points. Compared to weighted Voronoi stippling the convergence rate is much higher and the spectral and spatial properties are superior. Further, because points are created based on local operations, coherent stipple animations can be produced. Our method is also able to produce good quality point sets in other fields, such as remeshing of geometry, based on local geometric features such as curvature.