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GPU Applications

Imaging and Computer Vision

Computer vision and image processing algorithms are computationally intensive. With CUDA acceleration, applications can achieve interactive video frame-rate performance. Here we outline some of the work in the area of imaging and vision and point to some resources for developers.

Technical Reports on using CUDA for Imaging & Vision
> Biologically Inspired Computer Vision

Segmentation

> "CUDA cuts: Fast graph cuts on the GPU"

Machine Learning & Data Processing

> Hardware Efficient Belief Propagation
> Fast k nearest neighbor search using GPU
   


CUDA-Acceleration in Related Verticals
> ArrayFire GPU function library for C, C++, FORTRAN
> MATLAB®
> Medical imaging with CUDA-enabled GPUs
 
 
Core Software Kernels for Imaging and Vision on CUDA GPUs
> Level-Set segmentation with CUDA
> Video segmentation with CUDA
> Multiclass SVM implementation in CUDA
> Pedestrian Detection

SIFT (Scale Invariant Feature Transform)

> Marten Bjorkman's Implementation
> SiftGPU

Optical Flow

> Flowlib: Dense Optical Flow
> Baysian Optical Flow

Libraries and collections

> GPU4Vision
> OpenVIDIA: Popular computer vision algorithms on CUDA including
> MinGPU: A minimum GPU library for Computer Vision
> NVPP: NVIDIA Performance Primitives (Early access: Focuses on image and video processing)
See Also
> Tesla/CUDA Success stories
> Other Tesla Vertical Solutions
> CUDA Software development tools & libraries
> Buy Tesla
 
 

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ArrayFire is a trademark of AccelerEyes