Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
OpenVIDIA: parallel GPU computer vision
Proceedings of the 13th annual ACM international conference on Multimedia
On the computation of the Circle Hough Transform by a GPU rasterizer
Pattern Recognition Letters
A Method of Fast and Robust for Traffic Sign Recognition
ICIG '09 Proceedings of the 2009 Fifth International Conference on Image and Graphics
High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
Fast hough transform on GPUs: exploration of algorithm trade-offs
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
In-place transposition of rectangular matrices on accelerators
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
Parallel implementation of a real-time high dynamic range video system
Integrated Computer-Aided Engineering
| Hi-index | 0.00 |
Voting algorithms, such as histogram and Hough transforms, are frequently used algorithms in various domains, such as statistics and image processing. Algorithms in these domains may be accelerated using GPUs. Implementing voting algorithms efficiently on a GPU however is far from trivial due to irregularities and unpredictable memory accesses. Existing GPU implementations therefore target only specific voting algorithms while we propose in this work a methodology which targets voting algorithms in general. This methodology is used in gpu-vote, a framework to accelerate current and future voting algorithms on a GPU without significant programming effort. We classify voting algorithms into four categories. We describe a transformation to merge categories which enables gpu-vote to have a single implementation for all voting algorithms. Despite the generality of gpu-vote, being able to handle various voting algorithms, its performance is not compromised. Compared to recently published GPU implementations of the Hough transform and the histogram algorithms, gpu-vote yields a 11% and 38% lower execution time respectively. Additionally, we give an accurate and intuitive performance prediction model for the generalized GPU voting algorithm. Our model can predict the execution time of gpu-vote within an average absolute error of 5%.