A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
OpenVIDIA: parallel GPU computer vision
Proceedings of the 13th annual ACM international conference on Multimedia
Belief Propagation on the GPU for Stereo Vision
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Belief Propagation for Stereo Analysis of Night-Vision Sequences
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Residual Images Remove Illumination Artifacts!
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Optimizing and auto-tuning belief propagation on the GPU
LCPC'10 Proceedings of the 23rd international conference on Languages and compilers for parallel computing
Detecting, segmenting and tracking unknown objects using multi-label MRF inference
Computer Vision and Image Understanding
Fusion of 3D-LIDAR and camera data for scene parsing
Journal of Visual Communication and Image Representation
Hi-index | 0.01 |
Disparity map generation is a significant component of vision-based driver assistance systems. This paper describes an efficient implementation of a belief propagation algorithm on a graphics card (GPU) using CUDA (Compute Uniform Device Architecture) that can be used to speed up stereo image processing by between 30 and 250 times. For evaluation purposes, different kinds of images have been used: reference images from the Middlebury stereo website, and real-world stereo sequences, self-recorded with the research vehicle of the .enpeda.. project at The University of Auckland. This paper provides implementation details, primarily concerned with the inequality constraints, involving the threads and shared memory, required for efficient programming on a GPU.