|Title||Image synthesis using conditional random fields|
|Publication Type||Conference Paper|
|Year of Publication||2010|
|Authors||Ahmadi, E., Z. Azimifar, P. Fieguth, and S. Ayatollahi|
|Conference Name||17th IEEE International Conference on Image Processing (ICIP)|
|Keywords||Conditional Random Fields, image analysis, image classification, image sampling, image synthesis, large-scale morphological properties, Monte Carlo methods, porous media, Scientific Imaging|
Methods of scientific imaging and image analysis have become pervasive in a great variety of fields, including the properties of porous media. To study the large-scale morphological properties of porous media, high resolution random (Monte Carlo) samples are required. The purpose of this paper is to propose a novel approach for the statistical synthesis of scientific images, based on the concept of Conditional Random Fields. We explore two different sets of potential functions are used to model the pore-structure characteristics, and Monte Carlo Markov chain methods are also used to sample the high resolution images from the trained model. The resulting images are of high quality, and show the performance of the proposed framework.
Image synthesis using conditional random fields