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GTC On-Demand

Automotive
Presentation
Media
Tegra K1 and the Automotive Industry
Gernot Ziegler (NVIDIA), Timo Stich (NVIDIA)
Discover how mobile GPUs enable modern features of car driving in a power-efficient and standardized way, by providing the fundamental building blocks of computer vision to the higher-level reasoning functions that enable the car to detect lanes, par ...Read More
Discover how mobile GPUs enable modern features of car driving in a power-efficient and standardized way, by providing the fundamental building blocks of computer vision to the higher-level reasoning functions that enable the car to detect lanes, park automatically, avoid obstacles, etc. We explain the challenges of having to fit into a given time budget, and how the low-level machine vision such as corner detection, feature tracking and even more advanced functionality such as 3D surrounding reconstruction is achieved in the context of the car's systems and its outside environment.  Back
 
Keywords:
Automotive, Computer Vision, Machine Learning & Deep Learning, Mobile Applications, GTC 2014 - ID S4412
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Through the Eyes of a Car: Visualizing a Car's Camera System
Gernot Ziegler (NVIDIA)
Learn how the GPU's real-time graphics capabilities can be used to interactively visualize and enhance the camera system of modern cars. The GPU simplifies design, interactive calibration and testing of the car's computer vision systems, and even a ...Read More
Learn how the GPU's real-time graphics capabilities can be used to interactively visualize and enhance the camera system of modern cars. The GPU simplifies design, interactive calibration and testing of the car's computer vision systems, and even allows for creating simulated environments where the behavior of the car's computer vision can be tested to pass standard safety tests or navigational street situations.  Back
 
Keywords:
Automotive, Computer Vision and Machine Vision, Real-Time Graphics, GTC 2015 - ID S5123
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Developer - Algorithms
Presentation
Media
GPU-Accelerated Data Expansion for the Marching Cubes Algorithm
Chris Dyken, Gernot Ziegler
Learn how to accelerate marching cubes on the GPU by taking advantage of the GPU's high memory bandwidth and fast on-chip shared memory in a data expansion algorithm that can extract the complete iso-surface mesh from (dynamic) volume data w ...Read More

Learn how to accelerate marching cubes on the GPU by taking advantage of the GPU's high memory bandwidth and fast on-chip shared memory in a data expansion algorithm that can extract the complete iso-surface mesh from (dynamic) volume data without requiring any data transfers back to the CPU.

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Keywords:
Developer - Algorithms, Medical Imaging, Video and Image Processing, GTC 2010 - ID 2020
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Efficient Volume Segmentation on the GPU
Allan Rasmusson, Gernot Ziegler
- University of Aarhus, NVIDIA
Explore a new technique in the detection of common regions in a 2D/3D data array. Connected components along the axes are linked before actual label propagation starts. ...Read More
Explore a new technique in the detection of common regions in a 2D/3D data array. Connected components along the axes are linked before actual label propagation starts. The algorithm is completely gather-based, which allows for several optimizations in the CUDA C implementation. It enables real-time frame rates for the analysis of typical 2D images and interactive frame rates for the analysis of typical volume data.  Back
 
Keywords:
Developer - Algorithms, Computer Vision and Machine Vision, Medical Imaging, Video and Image Processing, GTC 2010 - ID 2021
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Summed Area Ripmaps
Gernot Ziegler (NVIDIA)
In this presentation, we show how ripmaps can replace Summed Area Tables (SATs) for the purpose of computing a large number of spatially varying box filter kernels throughout the input data, providing both higher accuracy and higher speed for ty ...Read More

In this presentation, we show how ripmaps can replace Summed Area Tables (SATs) for the purpose of computing a large number of spatially varying box filter kernels throughout the input data, providing both higher accuracy and higher speed for typical use cases. For this purpose, we demonstrate an implementation of ripmap generation in CUDA C (accelerated by shared memory usage), and a texture-cache based box filter for spatially varying kernel sizes, which can be implemented in both CUDA C and graphics-based APIs (e.g. OpenGL and DirectX).

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Keywords:
Developer - Algorithms, GTC 2012 - ID S2096
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Developer - Programming Languages
Presentation
Media
Analysis and Optimisation Part 1
Gernot Ziegler
- NVIDIA
 
Keywords:
Developer - Programming Languages, ISC 2011 - ID ISC1101
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Video and Image Processing
Presentation
Media
Connected Components Revisited on Kepler
Gernot Ziegler (NVIDIA)
Locating connected regions in images and volumes is a substantial building block in image and volume processing pipelines. We demonstrate how the Connected Components problem strongly benefits from a new feature in the Kepler architecture, direc ...Read More

Locating connected regions in images and volumes is a substantial building block in image and volume processing pipelines. We demonstrate how the Connected Components problem strongly benefits from a new feature in the Kepler architecture, direct thread data exchange through the SHUFFLE instruction.

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Keywords:
Video and Image Processing, Medical Imaging, GTC 2013 - ID S3193
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