I extended my superpixel filtering module so that it accepts an arbitrary input image representing pixel scores. Lighter pixels (lightness in the CIE L*a*b* colour space) are interpreted as more strongly preferring stippled rendering, whereas darker pixels are interpreted as more strongly preferring smooth rendering. The filter determines the average lightness value within each superpixel. Finally, it performs Otsuthresholding on the average lightness values to classify superpixels into two groups: Those which will be stippled, and those which will be rendered with smoothed colours only.
Consequently, I was free to experiment with a wide variety of methods for selecting superpixels for stippling or smooth rendering, without having to implement these methods.
In the full image stylization process, areas with high-frequency texture will be approximated by stipples, whereas areas with lower-frequency texture will be approximated by closed shapes. To create the closed shapes and decide how the image is to be divided into regions, each containing pixels with similar properties, I need an image segmentation algorithm.
For now, I have chosen Simple Linear Iterative Clustering (SLIC)  as the segmentation algorithm. SLIC is efficient and produces regions which adhere well to edges in the image. Moreover, it is not overly difficult to implement.
Implementing some of the ideas discussed in my previous post (http://gigl.scs.carleton.ca/node/545), I created a version of the leaky integration cumulative range geodesic filter which provides great flexibility for adaptive mask termination. The filter program uses the following algorithm for adaptive mask termination: