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.

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Technical Reports on using CUDA for Imaging & Vision

• Segmentation
• "CUDA cuts: Fast graph cuts on the GPU"
• Feature Processing
• Fast Scale Invariant Feature Detection and Matching on Programmable Graphics Hardware
• Fast Gain-Adaptive KLT Tracking on the GPU
• Stereo Imaging
• Stereo depth with a Unified Architecture GPU
• Machine Learning & Data Processing
• Large-scale Deep Unsupervised Learning using Graphics Processors
• Hardware Efficient Belief Propagation
• Fast k nearest neighbor search using GPU
• Biologically Inspired Computer Vision

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
• Stereo Vision
• Convolutions, Sobel, RMS, Histograms, Threshold, etc
• MinGPU: A minimum GPU library for Computer Vision
• NVPP: NVIDIA Performance Primitives (Early access: Focuses on image and video processing)

CUDA-Acceleration in Related Verticals

• CUDA-enabled GPU acceleration for MATLAB
• Medical imaging with CUDA-enabled GPUs

See Also

• Tesla/CUDA Success stories
• Other Tesla Vertical Solutions
• CUDA Software development tools & libraries