- Volume 15 Issue 4
DOI QR Code
Ship Detection Using Edge-Based Segmentation and Histogram of Oriented Gradient with Ship Size Ratio
- Received : 2015.12.14
- Accepted : 2015.12.24
- Published : 2015.12.25
In this paper, a ship detection method is proposed; this method uses edge-based segmentation and histogram of oriented gradient (HOG) with the ship size ratio. The proposed method can prevent a marine collision accident by detecting ships at close range. Furthermore, unlike radar, the method can detect ships that have small size and absorb radio waves because it involves the use of a vision-based system. This system performs three operations. First, the foreground is separated from the background and candidates are detected using Sobel edge detection and morphological operations in the edge-based segmentation part. Second, features are extracted by employing HOG descriptors with the ship size ratio from the detected candidate. Finally, a support vector machine (SVM) verifies whether the candidates are ships. The performance of these methods is demonstrated by comparing their results with the results of other segmentation methods using eight-fold cross validation for the experimental results.
Ship detection;Edge-based segmentation;Histogram of oriented gradient;Ship size ratio;Support vector machine
- D. Cornish and D. Dukette, The Essential 20: Twenty Components of an Excellent Health Care Team. Pittsburgh, PA: RoseDog Books, 2009.
- D. Y. Kim, G. K. Park, and H. Y. Kim, "A study on the ship information fusion with AIS and ARPA radar using by blackboard system," Journal of Korean Institute of Intelligent Systems, vol. 24, no. 1, pp. 16-21, 2014. http://dx.doi.org/10.5391/JKIIS.2014.24.1.016 https://doi.org/10.5391/JKIIS.2014.24.1.016
- H. Lee, E. K. Kim, and S. Kim, "A study on fuzzy logic based clustering method for radar data analysis," Journal of Korean Institute of Intelligent Systems, vol. 25, no. 3, pp. 217-222, 2015. http://dx.doi.org/10.5391/JKIIS.2015.25.3.217 https://doi.org/10.5391/JKIIS.2015.25.3.217
- Lloyd's Register Rulefinder, "COLREGS: International Regulations for Preventing Collisions at Sea," Available http://www.mar.ist.utl.pt/mventura/Projecto-Navios-I/IMO-Conventions%20(copies)/COLREG-1972.pdf
- H. J. Kim and J. H. Choi, "The phase error correction scheme using the iterative signal bandwidth estimation in SAR imaging system," in Proceedings of the Korean Institute of Intelligent Systems Conference, Daegu, Korea, 2000, pp. 463-646.
- F. C. Monteiro and A. Campilho, "Watershed framework to region-based image segmentation," presented at the 2008 19th International Conference on Pattern Recognition, Tampa, FL, December 8-11, 2008, pp. 1-4. http://dx.doi.org/10.1109/icpr.2008.4761587 https://doi.org/10.1109/icpr.2008.4761587
- G. Fritz, C. Seifert, L. Paletta, and H. Bischof, "Attentive object detection using an information theoretic saliency measure," in Attention and Performance in Computational Vision, L. Paletta, J. K. Tsotsos, E. Rome, and G. Humphreys, Eds. Berlin: Springer-Verlag, 2005, pp. 29-41.
- A. McAndrew, Introduction to Digital Image Processing with Matlab, 1st ed. Boston: Thompson Course Technology, 2004.
- Y. H. Baek and S. R. Moon, "Color edge detection using variable template operator, International Journal of Fuzzy Logic and Intelligent Systems, vol. 6, no. 2, pp. 116-120, 2006. http://dx.doi.org/10.5391/ijfis.2006.6.2.116 https://doi.org/10.5391/IJFIS.2006.6.2.116
- R. Milanese, S. Gil, and T. Pun, "Attentive mechanisms for dynamic and static scene analysis," Optical Engineering, vol. 34, no. 8, pp. 2428-2434, 1995. http://dx.doi.org/10.1117/12.205668 https://doi.org/10.1117/12.205668
- S. T. Seo, K. Sivakumar, and S. H. Kwon, "Dempster-Shafer's evidence theory-based edge detection," International Journal of Fuzzy Logic and Intelligent Systems, vol. 11, no. 1, pp. 19-24, 2011. http://dx.doi.org/10.5391/ijfis.2011.11.1.019 https://doi.org/10.5391/IJFIS.2011.11.1.019
- R. M. Haralick, S. R. Sternberg, and X. Zhuang, "Image analysis using mathematical morphology," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 4, pp. 532-550, 1987. http://dx.doi.org/10.1109/TPAMI.1987.4767941
- N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005, San Diego, CA, 2005, pp. 886-893. http://dx.doi.org/10.1109/CVPR.2005.177 https://doi.org/10.1109/CVPR.2005.177
- H. Eum, C. Yoon, H. Lee, and M. Park, "Continuous human action recognition using depth-MHI-HOG and a spotter model," Sensors, vol. 15, no. 3, pp. 5197-5227, 2015. http://dx.doi.org/10.3390/s150305197 https://doi.org/10.3390/s150305197
- C. G. Soares and T. Moan, "Model uncertainty in the long-term distribution of wave-induced bending moments for fatigue design of ship structures," Marine Structures, vol. 4, no. 4, pp. 295-315, 1991. http://dx.doi.org/10.1016/0951-8339(91)90008-y https://doi.org/10.1016/0951-8339(91)90008-Y
- P. F. Chen and C. H. Huang, "An inverse hull design problem in optimizing the desired wake of ship," Journal of Ship Research, vol. 46, no. 2, pp. 138-147, 2002.
- H. Byun and S. W. Lee, "A survey on pattern recognition applications of support vector machines," International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 3, pp. 459-486, 2003. http://dx.doi.org/10.1142/s0218001403002460 https://doi.org/10.1142/S0218001403002460
- H. Eum, J. Lee, C. Yoon, and M. Park, "Human action recognition for night vision using temporal templates with infrared thermal camera," in Proceedings of 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju, Korea, 2013, pp. 617-621. http://dx.doi.org/10.1109/URAI.2013.6677407 https://doi.org/10.1109/URAI.2013.6677407
- C. C. Chang and C. J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3. pp. 1-27, 2011. http://dx.doi.org/10.1145/1961189.1961199 https://doi.org/10.1145/1961189.1961199
- F. Perez-Cruz, J. Weston, D. J. L. Herrmann, and B. Scholkopf, "Extension of the nu-SVM range for classification," in Advances in Learning Theory: Methods, Models and Applications, J. Suykens, G. Horvath, S. Basu, C. Micchelli, and J. Vandewalle, Eds. Amsterdam: IOS Press, 2003, pp. 179-196.
- Y. Chen, X. S. Zhou, and T. S. Huang, "One-class SVM for learning in image retrieval," in Proceedings of 2001 International Conference on Image Processing, Thessaloniki, Greece, 2001, pp. 34-37. http://dx.doi.org/10.1109/icip.2001.958946 https://doi.org/10.1109/icip.2001.958946
- J. Park, J. Kim, H. Lee, and D. Park, "One-class support vector learning and linear matrix inequalities," International Journal of Fuzzy Logic and Intelligent Systems, vol. 3, no. 1, pp. 100-104, 2003. http://dx.doi.org/10.5391/ijfis.2003.3.1.100 https://doi.org/10.5391/IJFIS.2003.3.1.100
- D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63. 2011.
- Multi-Level Segmentation of Infrared Images with Region of Interest Extraction vol.16, pp.4, 2016, https://doi.org/10.5391/IJFIS.2016.16.4.246
- Depth-Based Recognition System for Continuous Human Action Using Motion History Image and Histogram of Oriented Gradient with Spotter Model vol.26, pp.6, 2016, https://doi.org/10.5391/JKIIS.2016.26.6.471
- Collaborative Learning based on Convolutional Features and Correlation Filter for Visual Tracking vol.16, pp.1, 2018, https://doi.org/10.1007/s12555-017-0062-x