IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel algorithms for line detection on a mesh
Journal of Parallel and Distributed Computing
Computing the Hough Transform on a Scan Line Array Processor (Image Processing)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implementation and evaluation of Hough transform algorithms on a shared-memory multiprocessor
Journal of Parallel and Distributed Computing - Special issue on shared-memory multiprocessors
A probabilistic algorithm for computing Hough transforms
Journal of Algorithms
Multiresolution Hough Transform-An Efficient Method of Detecting Patterns in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough transform algorithm for FPGA implementation
Signal Processing - Special section on information theoretic aspects of digital watermarking
Contribution to image and contours restoration
Real-Time Imaging
Segments Matching Using a Neural Network Approach
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
An approach to beacons detection for a mobile robot using a neural network model
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
On line mode incremental Hough transform implementation on Xilinx Fpga's
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
An approach to beacons detection for a mobile robot using a neural network model
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Resource-efficient FPGA architecture and implementation of hough transform
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Hough Transform (HT) is one of the most common methods for detecting shapes (lines, circles, etc.) in binary or edge images. Its advantage is its ability to detect discontinuous patterns in noisy images, but it requires a large amount of computing power. This paper presents a fast and enhanced version for computing the Incremental HT that was developed for digital device implementation. This algorithm does not require any Look Up Table or trigonometric calculations at run time. A number of the @r values generated in parallel is defined at the beginning of this algorithm. This algorithm leads to a significant reduction of the HT computation time and can be therefore used in real-time applications. This paper also provides some results obtained by this algorithm on several images.