Wyniki wyszukiwania - BazTech

1

A GPU-based method for approximate real-time fluid flow simulation

Rożen T., Boryczko K., Alda W.

Machine Graphics and Vision

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2008

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Vol. 17, No. 3

267-278

EN

Fluid flow can be realistically simulated by physical models. We present a method for simplifying the Navier-Stokes equations by relaxing the incompressibility constraint. Our method allows for low-cost real-time simulation of two-dimensional fluid flow with accuracy sufficient for computer graphics. The implementation takes advantage of recent programmable floating-point graphics hardware, which performs all the necessary computations.

2

Real-time ice visualisation on the GPU

Szajerman D., Pietruszka M.

Journal of Applied Computer Science

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2008

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Vol. 16, nr 2

89-106

EN

Today the most interactive graphics applications tend to render plausible realism using clever ideas of mapping subproblems of global illumination to Graphics Processing Units (GPU) or/and storing precomputed solutions in an appropriate manner. In this paper an empirical model of an ice-like material as an example of a multi-feature material is provided. Thanks to shaders whole presented rendering methods allow to work in interactive, on-line mode. A few datasets was used to check the performance of the rendering.

3

Minimizing CPU usage in soft shadow volumes algorithm

Lipowski J.

Machine Graphics and Vision

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2006

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Vol. 15, No. 3/4

493-503

EN

In this paper we present methods for reducing the CPU burden associated with the original implementation of the hardware-accelerated soft shadow volumes algorithm. We propose and implement two hardware-accelerated wedge construction methods: using the programmable vertex and fragment pipelines. New algorithms reducing the number of draw calls needed to render the shadow region (to three, two or even one) are also described. Tests performed using the current consumer level hardware show that our algorithms are no longer CPU-bound, and therefore can be over 2 times faster than the original algorithm.

4

Fast image registration in DirectX9 graphics hardware

Strzódka R., Droske M., Rumpf M.

Journal of Medical Informatics & Technologies

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2003

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Vol. 6

IT43--49

EN

The analysis of image time series requires a correlation of the information between two images. The gradient flow registration is a method for correlating this information by successively minimizing an appropriate energy along its gradient A graphics hardware implementation of this approach to image registration is presented. The gradient flow formulation makes use of a robust multi-scale regularization, an efficient multi-grid solver and an effective time-step control. The locality of the involved operations implies a data-flow which is very well suited for an acceleration in the streaming architecture of the DX9 graphics hardware. Therefore, the implementation obtains registration results at very high performance, registering two 2562 in less than 2 seconds, such that it could be used as an interactive tool in medical image analysis.

5

Non-rigid registration using hardware-accelerated free-form deformation

Soza G., Bauer M., Hastreiter P., Nimsky C., Greiner G.

Journal of Medical Informatics & Technologies

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2002

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Vol. 3

MI181--188

EN

In this paper we introduce a new method for non-rigid voxel-based registration of medical images. There exist many applications where an alignment between two image datasets has to be established. Often a registration of a time-shifted medical image sequence with appearing deformation of soft tissue (e.g. pre- and intraoperative data) has to be conducted. Soft tissue deformations are usually highly non-linear. In our approach, for the handling of this phenomenon and for obtaining an optimal non-linear alignment of respective datasets we transform one of them using 3D Bézier functions, which provides some inherent smoothness as well as elasticity. In order to find the optimal transformation, many evaluations of this Bézier function are necessary. In order to make the method more efficient, graphics hardware is extensively used. We applied our non-rigid algorithm successfully to MR brain images in several clinical cases and showed its value.