by Xu, Y, Chen, H, Klette, R, Liu, J and Vaudrey, T
Abstract:
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. © Springer-Verlag Berlin Heidelberg 2009.
Reference:
Belief propagation implementation using CUDA on an NVIDIA GTX 280 (Xu, Y, Chen, H, Klette, R, Liu, J and Vaudrey, T), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5866 LNAI, 2009.
Bibtex Entry:
@inproceedings{xu2009belief280,
author = "Xu, Y and Chen, H and Klette, R and Liu, J and Vaudrey, T",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "180--189",
title = "Belief propagation implementation using CUDA on an NVIDIA GTX 280",
volume = "5866 LNAI",
year = "2009",
abstract = "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. © Springer-Verlag Berlin Heidelberg 2009.",
doi = "10.1007/978-3-642-10439-8_19",
isbn = "364210438X",
isbn = "9783642104381",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
}