Paint by numbers: abstract image representations
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Processing images and video for an impressionist effect
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calligraphic Character Synthesis using Brush Model
CGI '97 Proceedings of the 1997 Conference on Computer Graphics International
Anisotropic Diffusion as a Preprocessing Step for Efficient Image Compression
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Very low bit-rate video coding based on matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
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We have developed a new stochastic image rendering method for the compression, description and segmentation of images. This paintbrush-like image transformation is based on a random searching to insert brush-strokes into a generated image at decreasing scale of brush-sizes, without predefined models or interaction. We introduced a sequential multiscale image decomposition method, based on simulated rectangular-shaped paintbrush strokes. The resulting images look like good-quality paintings with well-defined contours, at an acceptable distortion compared to the original image. The image can be described with the parameters of the consecutive paintbrush strokes, resulting in a parameter-series that can be used for compression. The painting process can be applied for image representation, segmentation and contour detection. Our original method is based on stochastic exhaustive searching which takes a long time of convergence. In this paper we propose a modified algorithm of speed up of about 2x where the faster convergence is supported by a dynamic Metropolis Hastings rule.