Image abstraction plays an important role in converting pixelated or low quality images into vector images. This paper presents a systematic user interactive process to abstract a generic photograph and to produce suitable content for vectorization. The goal of abstraction is to simplify the content of the photograph while retaining important information, creating a stylized and artistic image as a result. The system offers the user to create an importance map, by selecting regions with high importance or salience. Then, two different methods, bilateral filtering with k-means clustering and mean-shift segmentation, can be used as abstraction methods. Various parameters for different parts of the image and for each method, are exposed to the user to extend the spectrum of variety for the end result, and to tune the final image as desired. Canny's edge detection process is used to create a line drawing from the original image. The user can adjust thresholds, delete edges, and change the thickness and smoothness of edges to alter the line drawing. The resulting images are greatly diverse in terms of the number of colors and the amount of edges they can potentially have. In the end, this method proposes a new interactive way to create images suitable for vectorization, by combining and altering previous abstraction techniques.

This paper accelerates the rendering of diffusion curves using the ray tracing cores found on modern NVIDIA graphics cards using the method described by Bowers et al. [Bowers, Leahey and Wang 2011]. This method approximates the final result of the Poisson equation and in this paper is accelerated using Optix and dedicated ray tracing hardware. Using this method yielded a render time decrease of around 8 times between the Quadro P1000 and RTX 2060, while retaining the complex colour gradients and ease of use of traditional diffusion curves solved using the Poisson equation.

Minimalistic, low-poly images have gotten more popular in recent years. But making these low-poly images by hand can be time intensive. For this reason, a lot of research has gone into how to triangulate an image (semi-)automatically. In previous works, only constant color and bilinear interpolation have been used to color the triangles in the triangulation. In this paper, different coloring methods have been investigated and showcased on a fixed mesh. They show some promising first results, but these methods can still be improved upon. When that happens, these coloring methods, combined with previous works on how to optimally triangulate, may result in more accurate or visually pleasing low-poly images.

This research builds upon a previous method of vectorisation of pixel art by pixel neighbour connecting and boundary energy minimisation. The downside of the previous method is the lack of user input throughout the process and possible divergence between the results and the artist’s vision. The proposed method uses the first part of previous method to connect neighbour pixels and continues with a spring simulation of the boundaries. A process that can be heavily user-guided with adjusting the stiffness of the springs. Results show the proposed method to achieve higher variance in the results, possibly resulting in output images closer to the artist’s vision. However, introduce lengthy process for the user that can be removed by providing higher level GUI.