| Paper: | TA-P3.3 |
| Session: | Image and Video Modeling II |
| Time: | Tuesday, September 18, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
APPROXIMATION OF CONDITIONAL DENSITY OF MARKOV RANDOM FIELD AND ITS APPLICATION TO TEXTURE SYNTHESIS |
| Authors: |
Arnab Sinha; Indian Institute of Technology, Kanpur | | |
| | Sumana Gupta; Indian Institute of Technology, Kanpur | | |
| Abstract: |
Markov Random Field (MRF) based sampling method is popular for synthesizing natural textures. The main drawback of the synthesis procedure is the large computational complexity involved. In this paper, we propose an approximation of the conditional density description for the reduction of computational complexity required in sampling texture pixels from the conditional density. Assuming, Y belongs to lambda, and X belongs to lambda^d, we in this work studied the approximation of the conditional density function P(Y |X) as P(Y |theta(transpose)X), where theta belongs to R^d, is a unit vector. We have also shown that the classical gradient based optimization method is not suitable for finding the solution of theta. We have estimated theta using Genetic algorithm. The perceptual (visual) similartiy and neighborhood similarity measures between the textures synthesized using the full conditional description and approximated description, are shown for validating the method developed. |
