Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods

  • Tamás Szirányi
  • Zoltán Tóth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2134)


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.


Monte Carlo Markov Chain Exhaustive Search Anisotropic Diffusion Monte Carlo Markov Chain Method Target Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Tamás Szirányi
    • 1
    • 3
  • Zoltán Tóth
    • 2
    • 3
  1. 1.Analogical Computing Laboratory, Computer. & Automation Research InstitueHungarian Academy of SciencesHungary
  2. 2.Distributed System Department, Computer & Automation InstituteHungarian Academy of SciencesHungary
  3. 3.Department of Image Processing and NeurocomputingUniversity of VeszprémHungary

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