The Hough transform in the classification process of inland ships - Publikacja - MOST Wiedzy


The Hough transform in the classification process of inland ships


This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.


  • 0


  • 0

    Web of Science

  • 0


Informacje szczegółowe

Publikacja w czasopiśmie
artykuły w czasopismach
Opublikowano w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie strony 9 - 15,
ISSN: 1733-8670
Rok wydania:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.17402/331
Bibliografia: test
  1. Akiyama, T., Kobayashi, Y., Kishigami, J. & Muto, K. (2018) CNN-Based Boat Detection Model for Alert Sys- tem Using Surveillance Video Camera. In: 2018 IEEE 7 th Global Conference on Consumer Electronics, GCCE 2018, 8574704, Institute of Electrical and Electronics Engineers Inc., pp. 758-759. 7th IEEE Global Conference on Consum- er Electronics, GCCE 2018, Nara, Japan, 18/10/9. https:// otwiera się w nowej karcie
  2. Ali, M., Radzi, A. & Saad, H.M. (2017) A new approach to highway lane detection by using Hough transform tech- nique. Journal of ICT 16 (2), pp. 244-260.
  3. Bobkowska, K. (2016) Analysis of the objects images on the sea using Dempster-Shafer theory. 17 th International Ra- dar Symposium (IRS), 10-12 May 2016, Kraków, Poland. IEEE, 1-4. DOI: 10.1109/IRS.2016.7497280. otwiera się w nowej karcie
  4. Cohen, A.E., Cavallo, S.M., Coniglio, M.C. & Brooks, H.E. (2015) A Review of Planetary Boundary Layer Pa- rameterization Schemes and Their Sensitivity in Simulating Southeast U.S. Cold Season Severe Weather Environment. Weather and Forecasting 30 (3), pp. 591-612. otwiera się w nowej karcie
  5. Ferreira, J.C., Branquinho, J., Ferreira, P.C. & Piedade, F. (2017) Computer Vision Algorithms Fishing Vessel Mon- itoring Identification of Vessel Plate Number. In: De Paz J., Julián V., Villarrubia G., Marreiros G., Novais P. (eds) Am- bient Intelligence -Software and Applications -8 th Inter- national Symposium on Ambient Intelligence (ISAmI 2017). ISAmI 2017. Advances in Intelligent Systems and Comput- ing 615. Springer, Cham, pp. 9-17. otwiera się w nowej karcie
  6. Hyla, T. & Wawrzyniak, N. (2019) Automatic Ship Detec- tion on Inland Waters: Problems and a Preliminary Solu- tion. In Proceedings of ICONS 2019 The Fourteenth Inter- national Conference on Systems, IARIA, Valencia, Spain (pp. 56-60). otwiera się w nowej karcie
  7. Kanjir, U., Greidanus, H. & Oštir, K. (2018) Vessel de- tection and classification from spaceborne optical images: A literature survey. Remote Sensing of Environment 207, pp. 1-26. otwiera się w nowej karcie
  8. Koc-San, D., Selim, S., Aslan, N. & San, B.T. (2018) Au- tomatic citrus tree extraction from UAV images and digital surface models using circular Hough transform. Computers and Electronics in Agriculture 150, pp. 289-301. otwiera się w nowej karcie
  9. Meng, Y., Zhang, Z., Yin, H. & Ma, T. (2018) Automat- ic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron 106, pp. 34-41. otwiera się w nowej karcie
  10. Rawat, W. & Wang, Z. (2017) Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation 29 (9), pp. 2352-2449. otwiera się w nowej karcie
  11. Shao, Z., Wang, L., Wang, Z., Du, W. & Wu, W. (2019) Saliency-Aware Convolution Neural Network for Ship De- tection in Surveillance Video. IEEE Transactions on Cir- cuits and Systems for Video Technology. DOI: https://doi. org/10.1109/TCSVT.2019.2897980 otwiera się w nowej karcie
  12. Shrivakshan, G.T. & Chandrasekar, C. (2012) A Com- parison of various Edge Detection Techniques used in Image Processing. IJCSI International Journal of Computer Sci- ence Issues 9, 5 (1), pp. 269-276.
  13. Solmaz, B., Gundogdu, E., Yucesoy, V., Koç, A. & Alatan, A.A. (2018) Fine-grained recognition of mari- time vessels and land vehicles by deep feature embedding. IET Computer Vision 12 (8), pp. 1121-1132. otwiera się w nowej karcie
  14. Turker, M. & Koc-San, D. (2015) Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. Internation- al Journal of Applied Earth Observation and Geoinforma- tion 34, pp. 58-69. otwiera się w nowej karcie
  15. Wang, C., Jiang, S., Zhang, H., Wu, F. & Zhang, B. (2014) Ship detection for high-resolution SAR images based on feature analysis. IEEE Geoscience and Remote Sensing Letters 11 (1), pp. 119-123. otwiera się w nowej karcie
  16. Wawrzyniak, N. & Hyla, T. (2019) Automatic Ship Iden- tification Approach for Video Surveillance Systems. In Pro- ceedings of ICONS 2019 The Fourteenth International Con- ference on Systems, IARIA, Valencia, Spain (pp. 65-68). otwiera się w nowej karcie
  17. Wawrzyniak, N. & Stateczny, A. (2018) Automatic Wa- tercraft Recognition and Identification on Water Areas Cov- ered by Video Monitoring as Extension for Sea and River Traffic Supervision Systems. Polish Maritime Research 25 (s1), pp. 5-13. otwiera się w nowej karcie
  18. Zhang, M.M., Choi, J., Daniilidis, K., Wolf, M.T. & Kanan, C. (2015) Vais: A dataset for recognizing mari- time imagery in the visible and infrared spectrums. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 7-12 June 2015, Boston, MA USA, pp. 10-16. otwiera się w nowej karcie
Politechnika Gdańska

wyświetlono 29 razy

Publikacje, które mogą cię zainteresować

Meta Tagi