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.
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Technical Reports on using CUDA for Imaging & Vision
- Segmentation
- Feature Processing
- Fast Scale Invariant Feature Detection and Matching on Programmable Graphics Hardware
- Fast Gain-Adaptive KLT Tracking on the GPU
- Stereo Imaging
- 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)
- 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)
