EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

Paweł Kowalski

pawel.kowalski@pg.edu.pl
Gdansk University of Technology (Poland)
http://orcid.org/0000-0002-0913-1408

Piotr Tojza


Gdansk University of Technology (Poland)
http://orcid.org/0000-0002-0837-0976

Abstract

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from
-45˚ to 45˚.  Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.


Keywords:

image processing, real-time processing, Hough transform, straight lines detection

Ballard D. H.: Generalizing the hough transform to detect arbitrary shapes. Pattern recognition 13(2), 1981, 111–122.
DOI: https://doi.org/10.1016/0031-3203(81)90009-1   Google Scholar

Elhossini A., Moussa M.: Memory efficient FPGA implementation of hough transform for line and circle detection. 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2012, 1–5
DOI: https://doi.org/10.1109/CCECE.2012.6335003   Google Scholar

Guan J., An F., Zhang X., Chen L., Mattausch H. J.: Real-time straight-line detection for xga-size videos by hough transform with parallelized voting procedures. Sensors 17(2), 2017, 270.
DOI: https://doi.org/10.3390/s17020270   Google Scholar

Han Q., Zhao K., Xu J., Cheng M. M.: Deep hough transform for semantic line detection. 2020, arXiv preprint arXiv:2003.04676.
DOI: https://doi.org/10.1007/978-3-030-58545-7_15   Google Scholar

Illingworth J., Kittler J.: A survey of the hough trans form. Computer vision, graphics, and image processing 44(1), 1988, 87–116.
DOI: https://doi.org/10.1016/S0734-189X(88)80033-1   Google Scholar

Kowalski P., Smyk R.: Straight lines detection in digital image using hough transform. Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdanskiej 61, 2018, 45–48.
  Google Scholar

Milletari F., Ahmadi S. A., Kroll C., Plate A., Rozanski V., Maiostre J., Levin J., Dietrich O., Ertl-Wagner B., Bötzel K., et al.: Hough-cnn: deep learning for segmentation of deep brain regions in mri and ultrasound. Computer Vision and Image Understanding 164, 2017, 92–102.
DOI: https://doi.org/10.1016/j.cviu.2017.04.002   Google Scholar

Mukhopadhyay P., Chaudhuri B. B.: A survey of hough transform. Pattern Recognition 48(3), 2015, 993–1010.
DOI: https://doi.org/10.1016/j.patcog.2014.08.027   Google Scholar

Ritchie D. M., Kernighan W., Lesk M. E.: The C programming language. Prentice Hall Englewood Cliffs, 1988.
  Google Scholar

Serra P. L., Masotti P. H., Rocha M. S., de Andrade D. A., Torres W. M., de Mesquita R. N.: Two-phase flow void fraction estimation based on bubble image segmentation using randomized hough transform with neural network (rhtn). Progress in Nuclear Energy 118, 2020, 103133.
DOI: https://doi.org/10.1016/j.pnucene.2019.103133   Google Scholar

Shehata Hassanein A., Mohammad S., Sameer M., Ehab Ragab M.: A survey on hough transform, theory, techniques and applications. 2015, arXiv:1502.02160.
  Google Scholar

Ye H., Shang G., Wang L., Zheng M.: A new method based on hough transform for quick line and circle detection. IEEE 8th International Conference on Biomedical Engineering and Informatics (BMEI), 2015, 52–56.
DOI: https://doi.org/10.1109/BMEI.2015.7401472   Google Scholar

Download


Published
2021-12-20

Cited by

Kowalski, P., & Tojza, P. (2021). EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 11(4), 22–27. https://doi.org/10.35784/iapgos.2817

Authors

Paweł Kowalski 
pawel.kowalski@pg.edu.pl
Gdansk University of Technology Poland
http://orcid.org/0000-0002-0913-1408

Authors

Piotr Tojza 

Gdansk University of Technology Poland
http://orcid.org/0000-0002-0837-0976

Statistics

Abstract views: 263
PDF downloads: 178