Humanoid robot painter: Visual perception and high-level planning

Abstract -This paper presents visual perception discovered in high-level manipulator planning for a robot to reproduce the procedure involved in human painting. First, we apply a technique of 2D object segmentation that considers region similarity as an objective function and edge as a constraint with artificial intelligent used as a criterion function. The system can segment images more effectively than most of existing methods, even if the foreground is very similar to the background. Second, we propose a novel color perception model that shows similarity to human perception. The method outperforms many existing color reduction algorithms. Third, we propose a novel global orientation map perception using a radial basis function. Finally, we use the derived model along with the brush's position-and force-sensing to produce a visual feedback drawing. Experiments show that our system can generate good paintings including portraits

Neural Network Based Foreground Segmentation with an Application to Multi-Sensor 3D Modeling

This paper presents a technique for foreground/background segmentation using either color images or a combination of color and range images. In the case of images captured from a single 2D camera, a hybrid experience-based foreground segmentation technique is developed using a neural network and graph cut paradigm. This gives an advantage over methods that are based on color distribution or gradient information if the foreground/background color distributions are not well separated or the boundary is not clear. The system can segment images more effectively than the latest technology of graph cut, even if the foreground is very similar to the background. It also shows how to use the method for multi-sensor based 3D modeling by segmenting the foreground of each viewpoint in order to generate 3D models