Water wave visualization simulation using feedback of image texture analysis

In order to improve the visualization simulation effect of water wave, we use the images of actual water wave as a feedback to correct the control parameters of Shallow Water (SW) equations in this paper. First, we employ a kind of simplified numerical method to resolve the SW equations and create an initial water wave animation. The initial control parameters of SW equations can be set arbitrarily. Second we cut some water wave images from the artificial animation above to build an image dataset of Simulated Water Wave (SWW). Third, we capture the Actual Water Wave (AWW) images by cameras, which are fixed in the selected locations of a moving boat, to build another image dataset. After that, we make a correlation analysis of image texture between the artificial image dataset of SWW and the actual image dataset of AWW to compare their similarity. In this phase, some image quality metrics of Tamura’s texture, together with the mathematic tools of Mojette transform, Gabor wavelet and Detrended Fluctuation Analysis (DFA) technique are utilized to accomplish the static and dynamic texture analysis tasks. Finally, we use the results of correlation analysis above as a feedback to give a guidance to tune the control parameters of SW equations and regenerate the water wave animation with better visualization effects. To enhance the fidelity of SWW images, we also use Gabor wavelet and the criterion of minimized distance to estimate the environment illumination direction of AWW if the texture definition is good enough. By this means, we can set proper light source parameters to the visualization animation of SWW. Extensive experiment results have shown us that the visualization simulation effect can be improved effectively by the application of our texture feedback based techniques.