This paper puts forward a hierarchical method of fluid surface modeling in natural landscapes. The proposed method produces a visually plausible surface geometry with the texture from a single video image recorded by a standard video device. In contrast with the conventional physically based fluid simulation, our method computes preliminary results using empirical method and adopts Stokes wave model to obtain the reconstruction result. We illustrate the working of system with a wide range of possible scene, and a qualitative evaluation of our method is provided to verify the quality of the surface geometry. The experiment shows that the method can meet the requirement of real-time performance and the reality of the fluid.

Customizing a desired naturalistic fluid simulation result from video to obtain similar artistic effect is significant in practice. But art-directed customizing is challengeable due to the chaotic nature of the physics contained in it, and this still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. This paper focuses on the problem of physically based fluid re-simulation which is foundational to customize desired naturalistic simulation result from video example. In the previous achievements, conventional algorithms primarily recover 3D geometry of fluid surface or obtain the velocity of fluid particles in video. However, to launch new derivative results, just geometry and velocity are not enough, and physically based driven models are promising. We present a novel method that is capable of efficiently recovering physically driven model from an existing video. We advocate a new approach to calculate the velocity and non-normalized surface geometry under the constraints of the appearance and dynamic behavior of the example fluid in multi-scale framework, and use the calculated physical properties quickly to recover the fluid-driven model. In particular, to calculate the surface geometry more accurately, we introduce a scale factor between normalized geometry and non-normalized geometry to acquire a more accurate result. We propose a novel recovery algorithm, in which the particle densities of lattice Boltzmann method can be recovered more accurately from matching fluid advection geometry with the calculated non-normalized geometry. Fluid re-simulations with different types of density can be achieved, including constrained particle density, auto-advection density, and enhanced particle density. We demonstrate our results in several challenging scenarios and provide qualitative evaluation to our method. Some applications based on our approach are also demonstrated in the implementation.

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Dynamic texture is a part of the natural scene. Dynamic texture segmentation issue is research hotspot in the field of computer vision and even robot research area. This paper presents a new dynamic texture recognition method based on level set strategy. The level set function evolutes to the boundary of objects according the optical flow feature of moving objects. Because we use the optical flow based terminal condition, the evolution can fast stop to the edge of dynamic texture object. The new method has the characteristic of fast and accuracy. It overcomes the shortcoming of reinitializing and slow evolution speed in the existing methods. The experimental results show that the method can effectively achieve the dynamic texture segmentation of moving objects and it is a very useful method.