Robert Strzodka

Level set segmentation in graphics hardware

Implicit active contours are a very flexible technique in the segmentation of digital images. A novel type of hardware implementation is presented here to approach real time applications We propose to exploit the high performance of modern graphics cards for numerical computations. Vectors are regarded as images and linear algebraic operations on vectors are realized by the graphics operations of image blending. Thus, the performance benefits from the high memory bandwidth and the economy of command transfers, while the restricted precision does not infect the qualitative behavior of the level set propagation Here, we pick up a first order solver for the basic implicit level set model and present an implementation performing at 2 ms for an explicit timestep on a 128/sup 2/ image.

Generalized distance transforms and skeletons in graphics hardware

We present a framework for computing generalized distance transforms and skeletons of two-dimensional objects using graphics hardware. Our method is based on the concept of footprint splatting. Combining different splats produces weighted distance transforms for different metrics, as well as the corresponding skeletons and Voronoi diagrams. We present a hierarchical acceleration scheme and a subdivision scheme that allows visualizing the computed skeletons with subpixel accuracy in real time. Our splatting approach allows one to easily change all the metric parameters, treat any 2D boundaries, and easily produce both DTs and skeletons. We illustrate the method by several examples.

Real-Time Motion Estimation and Visualization on Graphics Cards

We present a tool for real-time visualization of motion features in 2D image sequences. The motion is estimated through an eigenvector analysis of the spatio-temporal structure tensor at every pixel location. This approach is computationally demanding but allows reliable velocity estimates as well as quality indicators for the obtained results. We use a 2D color map and a region of interest selector for the visualization of the velocities. On the selected velocities we apply a hierarchical smoothing scheme which allows the choice of the desired scale of the motion field. We demonstrate several examples of test sequences in which some persons are moving with different velocities than others. These persons are visually marked in the real-time display of the image sequence. The tool is also applied to angiography sequences to emphasize the blood flow and its distribution. An efficient processing of the data streams is achieved by mapping the operations onto the stream architecture of standard graphics cards. The card receives the images and performs both the motion estimation and visualization, taking advantage of the parallelism in the graphics processor and the superior memory bandwidth. The integration of data processing and visualization also saves on unnecessary data transfers and thus allows the real-time analysis of 320/spl times/240 images. We expect that on the newest generation of graphics hardware our tool could run in real time for the standard VGA format.

Image Registration by a Regularized Gradient Flow. A Streaming Implementation in DX9 Graphics Hardware

Abstract.The presented image registration method uses a regularized gradient flow to correlate the intensities in two images. Thereby, an energy functional is successively minimized by descending along its regularized gradient. The gradient flow formulation makes use of a robust multi-scale regularization, an efficient multi-grid solver and an effective time-step control. The data processing is arranged in streams and mapped onto the functionality of a stream processor. This arrangement automatically exploits the high data parallelism of the problem, and local data access helps to maximize throughput and hide memory latency. Although dedicated stream processors exist, we use a DX9 compatible graphics card as a stream architecture because of its ideal price-performance ratio. The new floating point number formats guarantee a sufficient accuracy of the algorithm and eliminate previously present concerns about the use of graphics hardware for medical computing. Therefore, the implementation achieves reliable results at very high performance, registering two 2572 images in approximately 3sec, such that it could be used as an interactive tool in medical image analysis.

Nonlinear diffusion in graphics hardware

Multiscale methods have proved to be successful tools in image denoising, edge enhancement and shape recovery. They are based on the numerical solution of a nonlinear diffusion problem where a noisy or damaged image which has to be smoothed or restorated is considered as initial data. Here a novel approach is presented which will soon be capable to ensure real time performance of these methods. It is based on an implementation of a corresponding finite element scheme in texture hardware of modern graphics engines. The method regards vectors as textures and represents linear algebra operations as texture processing operations. Thus, the resulting performance can profit from the superior bandwidth and the build in parallelism of the graphics hardware. Here the concept of this approach is introduced and perspectives are outlined picking up the basic Perona Malik model on 2D images.

Graphics Processor Units: New Prospects for Parallel Computing

This chapter provides an introduction to the use of Graphics Processor Units (GPUs) as parallel computing devices. It describes the architecture, the available functionality and the programming model. Simple examples and references to freely available tools and resources motivate the reader to explore these new possibilities. An overview of the different applications of GPUs demonstrates their wide applicability, yet also highlights limitations of their use. Finally, a glimpse into the future of GPUs sketches the growing prospects of these inexpensive parallel computing devices.

Dynamic adaptive shadow maps on graphics hardware

Author(s): Lefohn, Aaron; Sengupta, Shubhabrata; Kniss, Joe M.; Strzodka, Robert; Owens, John D. | Abstract: We present a novel implementation of adaptive shadow maps (ASMs) that performs all shadow lookups and scene analysis on the GPU, enabling interactive rendering with ASMs while moving both the light and camera. Adaptive shadow maps offer a rigorous solution to projective and perspective shadow map aliasing while maintaining the simplicity of a purely image-based technique. The complexity of the ASM data structure, however, has prevented full GPU-based implementations until now. Our approach uses an entirely GPU-based data structure and a blend of graphics and GPU stream programming. We support shadow map effective resolutions up to 131,072 x 131,072 and, unlike previous implementations, provide smooth transitions between resolution levels by trilinearly filtering (mipmapping) the shadow lookups.

Octree textures on graphics hardware

Author(s): Kniss, Joe M.; Lefohn, Aaron; Strzodka, Robert; Sengupta, Shubhabrata; Owens, John D. | Abstract: We implement an interactive 3D painting application that stores paint in an octree-like GPU-based adaptive data structure. Interactive painting of complex or unparameterized surfaces is an important problem in the digital film community. Many models used in production environments are either difficult to parameterize or are unparameterized implicit surfaces. We address this problem with a system that allows interactive 3D painting of complex, unparameterized models. The included movie demonstrates interactive painting of a 817k polygon model with effective paint resolutions varying between 64^3 to 2048^3. Our implementation differs from previous work in two important ways: first, it uses an adaptive data structure implemented entirely on the GPU, and second, it enables interactive performance with high quality by supporting quadlinear (mipmapped) filtering and fast, constant-time data accesses.