Applying Medical Imaging Tractography Techniques to Painterly Rendering of Images
This work presents an exploratory cross-domain application of medical imaging methods to computer graphics, which is incremental as it adapts existing techniques to a new domain.
This paper tackles the problem of generating painterly renderings of images by applying medical imaging tractography techniques, specifically using a tractography algorithm to place brush strokes that mimic human artists' painting processes, and demonstrates the technique on portraits and general images.
Doctors and researchers routinely use diffusion tensor imaging (DTI) and tractography to visualize the fibrous structure of tissues in the human body. This paper explores the connection of these techniques to the painterly rendering of images. Using a tractography algorithm the presented method can place brush strokes that mimic the painting process of human artists, analogously to how fibres are tracked in DTI. The analogue to the diffusion tensor for image orientation is the structural tensor, which can provide better local orientation information than the gradient alone. I demonstrate this technique in portraits and general images, and discuss the parallels between fibre tracking and brush stroke placement, and frame it in the language of tractography. This work presents an exploratory investigation into the cross-domain application of diffusion tensor imaging techniques to painterly rendering of images. All the code is available at https://github.com/tito21/st-python