Constant-Time Hough Transform on a 3D Reconfigurable Mesh Using Fewer Processors
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
A Fast Efficient Parallel Hough Transform Algorithm on LARPBS
The Journal of Supercomputing
On the computation of the Circle Hough Transform by a GPU rasterizer
Pattern Recognition Letters
Recognition of circular patterns on GPUs: Performance analysis and contributions
Journal of Parallel and Distributed Computing
Using Graphics Hardware for Enhancing Edge and Circle Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
A compact system for real-time detection of line segments
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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The Hough transform is an important problem in image processing and computer vision. An efficient algorithm for computing the Hough transform has been proposed on a reconfigurable array by Kao et al. (1995). For a problem with an √N×√N image and an n×n parameter space, the algorithm runs in a constant time on a three-dimensional (3-D) n×n×N reconfigurable mesh where the data bus is N1c/-bit wide. To our best knowledge, this is the most efficient constant-time algorithm for computing the Hough transform on a reconfigurable mesh. In this paper, an improved Hough transform algorithm on a reconfigurable mesh is proposed. For the same problem, our algorithm runs in constant time on a 3-D n*n×n×√n√n reconfigurable mesh, where the data bus is only log N-bit wide. In most practical situations, n=O(√N). Hence, our algorithm requires much less VLSI area to accomplish the same task. In addition, our algorithm can compute the Radon transform (a generalized Hough transform) in O(1) time on the same model, whereas the algorithm in the above paper cannot be adapted to computing Radon transform easily