Accurate Centerline Detection and Line Width Estimation of Thick Lines Using the Radon Transform

• Published in: IEEE transactions on image processing Vol. 16; no. 2; pp. 310 - 316
• Main Authors: Qiaoping Zhang, Couloigner, I.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Application software; Applied sciences; Artificial Intelligence; Computer science; control theory; systems; Computer vision; Detectors; Displays; Exact sciences and technology; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image line pattern analysis; Image processing; Image resolution; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Information, signal and communications theory; Networks; Noise cancellation; object detection; Pattern recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Radar detection; Radon; radon transforms; Remote sensing; Reproducibility of Results; Roads; Robustness; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Synthetic aperture radar; Telecommunications and information theory; Transforms; Computer vision; Image line pattern analysis; High resolution; Road network; Shape detection; Remote sensing; Noisy image; Selection problem; Image analysis; Line width; Linear transformation; radon transforms; Computer applications; Object detection; Signal processing; Feature extraction; Robustness; Pattern analysis; Edge detection; Radon transformation; Target detection; Non contact measurement
• DOI: 10.1109/TIP.2006.887731
• ISSN: 1057-7149

Centerline detection and line width estimation are important for many computer vision applications, e.g., road network extraction from high resolution remotely sensed imagery. Radon transform-based linear feature detection has many advantages over other approaches: for example, its robustness in noisy images. However, it usually fails to detect the centerline of a thick line due to the peak selection problem. In this paper, several key issues that affect the centerline detection using the radon transform are investigated. A mean filter is proposed to locate the true peak in the radon image and a profile analysis technique is used to further refine the line parameters. The thetas-boundary problem of the radon transform is also discussed and the erroneous line parameters are corrected. Intensive experiments have shown that the proposed methodology is effective in finding the centerline and estimating the line width of thick lines