The Radon transform and its application to shape parametrization in machine vision
Image and Vision Computing - Special issue: papers from the second Alvey Vision Conference
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A probabilistic Hough transform
Pattern Recognition
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
A robust Hough transform technique for complete line segment description
Real-Time Imaging
Deriving stopping rules for the probabilistic Hough transform by sequential analysis
Computer Vision and Image Understanding
An optimizing line finder using a Hough transform algorithm
Computer Vision and Image Understanding
Hough Transform Modified by Line Connectivity and Line Thickness
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guaranteed convergence of the Hough transform
Computer Vision and Image Understanding
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Robust detection of lines using the progressive probabilistic Hough transform
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Strip line detection and thinning by RPCL-based local PCA
Pattern Recognition Letters
Accurate and robust line segment extraction by analyzing distribution around peaks in Hough space
Computer Vision and Image Understanding
A Parallel-Line Detection Algorithm Based on HMM Decoding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended Hough transform for linear feature detection
Pattern Recognition
A Bayesian approach to the Hough transform for line detection
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, an improved Hough transform (HT) method is proposed to robustly detect line segments in images with complicated backgrounds. The work focuses on detecting line segments of distinct lengths, totally independent of prior knowledge of the original image. Based on the characteristics of accumulation distribution obtained by conventional HT, a local operator is implemented to enhance the difference between the accumulation peaks caused by line segments and noise. Through analysis of the effect of the operator, a global threshold is obtained in the histogram of the enhanced accumulator to detect peaks. Experimental results are provided to demonstrate the efficiency and robustness of the proposed method.