Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot.

Robot artistic painting and robot calligraphy do require brush models for brushstroke simulation and painting robot control. One of the main features of the brush is its compliance, which describes the relationship between the brush footprint shape and the pressure applied to the brush. In addition, during motion, the brush footprint position lags from the brush handle position in a complicated manner. To date, the question of creating a physically correct model of these effects and choosing the best method for the model parameter calibration has not been presented in the literature. In the current paper, we derive equations of the brush contact patch motion, give their closed-form solutions, and investigate three methods for the brush model calibration: capturing brush footprints on a matte glass with a camera, painting calibration brushstrokes, and capturing a brush shape side projection with a camera. As we show, calibration brushstrokes give us primary information on brush contact patch displacement during painting, and capturing the brush side projection allows the accurate estimation of the gap from the brush tip to the center of the contact patch. Capturing brush footprints is useful for creating a brushstroke executable model. As an example, a model for a round artistic brush was created and verified in three tests, including measuring the coordinates of an angular brushstroke center line, simulating an angular brushstroke, and writing a signature using a robotic setup.

Non-photorealistic rendering (NPR) with explicit brushstroke representation is essential for both high-grade imitating of artistic paintings and generating commands for artistically skilled robots. Some algorithms for this purpose have been recently developed based on simple heuristics, e.g., using an image gradient for driving brushstroke orientation. The notable drawback of such algorithms is the impossibility of automatic learning to reproduce an individual artist’s style. In contrast, popular neural style transfer (NST) algorithms are aimed at this goal by their design. The question arises: how good is the performance of neural style transfer methods in comparison with the heuristic approaches? To answer this question, we develop a novel method for experimentally quantifying brushstroke rendering algorithms. This method is based on correlation analysis applied to histograms of six brushstroke parameters: length, orientation, straightness, number of neighboring brushstrokes (NBS-NB), number of brushstrokes with similar orientations in the neighborhood (NBS-SO), and orientation standard deviation in the neighborhood (OSD-NB). This method numerically captures similarities and differences in the distributions of brushstroke parameters and allows comparison of two NPR algorithms. We perform an investigation of the brushstrokes generated by the heuristic algorithm and the NST algorithm. The results imply that while the neural style transfer and the heuristic algorithms give rather different parameter histograms, their capabilities of mimicking individual artistic manner are limited comparably. A direct comparison of NBS-NB histograms of brushstrokes generated by these algorithms and of brushstrokes extracted from a real painting confirms this finding.