Automatic Parametrisation for an Image Completion Method Based on Markov Random Fields
Recently, a new exemplar-based method for image completion, texture synthesis and image inpainting was proposed which uses a discrete global optimization strategy based on Markov Random. Fields. Its main advantage lies in the use of priority belief propagation and dynamic label, pruning to reduce the computational cost of standard belief propagation while producing high quality results. However, one of the drawbacks of the method is its use of a heuristically chosen parameter set. In this...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 2007 IEEE International Conference on Image Processing (ICIP-2007)|
|01_Ho_Automatic_Parametrisation_for_2007.pdf||327.57 kB||Adobe PDF||Request a copy|
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