Vol. 7 (06) pp. 888-899 DOI: 10.21474/IJAR01/9292

DETECTION AND RECOGNITION OF BANGLADESHI FISHES USING SURF AND CONVOLUTIONAL NEURAL NETWORK.

, , , and
  • Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
61 Downloads 206 Views
Crossref

Download Full Paper (PDF)

Abstract

This paper represents a model to detection and recognize local fishes of Bangladesh implementing image processing and neural networking approaches. The aim of the research work is to apply computer vision and AI techniques so that people of next generation can recognize Bangladeshi fishes as most of the young people in city, have less idea to classify traditional and deshi fishes. We implemented our custom Dataset consisting of 400 sample images for the experiment method to measure out its credibility. In the proposed, model sequential grassfire algorithm is used along with pre-processing techniques like noise cancelation, gray scaling, flood-fill method, binarization to detect and analysis shape of fish. Further, to do classification and recognition of the detected fishes, convolutional neural network (CNN) and method of Speeded up robust feature (SURF) had been applied to visualize difference between to techniques. CNN architecture got better accuracy with 90.9% score for recognition and classify fishes where SURF algorithm visualize better recognition matching putative points after extracting features.

Keywords

Article Analytics

References

  1. Wikipedia contributors. Fish. In?Wikipedia, The Free Encyclopedia. Available:?https://en.wikipedia.org/w/index.php?title=Fish&oldid=851610198[Accessed April 15, 2018].
  2. Andr?s?Hern?ndez-Serna​,?Luz Fernanda?Jim?nez-Segura((Nov,2014)),Automatic identification of species with neural networks Chadimova, L. (2015). Creation of 3D models of chosen historical buildings for supporting knowledge transfer. 2015 Digital Heritage.
  3. Singh and M. Sachan, "Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition," 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2014, pp. 1-5.
  4. L. Goh, K. H. Lim, A. A. Gopalai and Y. Z. Chong, "Multilayer perceptron neural network classification for human vertical ground reaction forces," 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, 2014, pp. 536-540.
  5. Al-Omari, S.A.K., P. Sumari, S.A. Al-Taweel and A.J.N. Husain, 2009. Digital recognition using neural network. J. Comput. Sci., 5: 427-434.
  6. Bai, X., X. Yang and J.L. Latecki, 2008. Detection and recognition of contour parts based on shape similarity. Patt. Recog., 41: 2189-2199.
  7. Alsmadi, M.K.S., K.B. Omar, S.A. Noah and I.Almarashdeh, 2009. Fish recognition based on the combination between robust features selection, image segmentation and geometrical parameters techniques using artificial neural network and decision tree. Int. J. Comput. Sci. Inform. Secur.,6: 215-221.
  8. Storbecka and B. Daan, ?Fish species recognition using computer vision and a neural network,? Fisheries Research, vol. 51, pp. 11?15, 2001.
  9. Spampinato, Y.-H. Chen-Burger, G. Nadarajan, and R. B. Fisher, "Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos," VISAPP (2), vol. 2008, pp. 514-519, 2008
  10. -C. Chuang, J.-N. Hwang, K. Williams, and R. Towler, "Automatic fish segmentation via double local thresholding for trawl-based
  11. Sonka., V. Hlavac. and R. Boyle., "Image pre-processing," in Image Processing, Analysis and Machine Vision., Boston, Springer, 1993, pp. 56-111
  12. EngineersGarage, "Introduction to Image Processing," [onine] . Available: https://www.engineersgarage.com/articles/image-processing-tutorial-applications. [Accessed May 2, 2018]
  13. Kumar, "Image pre processing," 06 12 2012. [Online]. Retrieved From https://www.slideshare.net/ASHI14march/image-pre-processing. [Accessed 21 07 2018].
  14. Guo, X. Qu, X. Du, K. Wu and X. Chen, "Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation," Advances in Multimedia, vol. 2014, pp. 1-14, 2014.
  15. Kutskir, "Fastest Gaussian Blur (in linear time)," [Online]. Available: http://blog.ivank.net/fastest-gaussian-blur.html. [Accessed 21 07 2018].
  16. Jason Corso, "Linear Filters and Image Processing," [Online]. Available: https://web.eecs.umich.edu/~jjcorso/t/598F14/files/lecture_0924_filtering.pdf [Accessed 21 July, 2018]
  17. Roman and J. Woodruff, "Ideal binary masking in reverberation," 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), Bucharest, 2012, pp. 629-633.
  18. Mischie, "On using time-frequency binary masking for dereverberation," International Symposium on Signals, Circuits and Systems ISSCS2013, Iasi, 2013, pp. 1-4. doi: 10.1109/ISSCS.2013.6651226
  19. M. Nosal, "Flood-fill algorithms used for passive acoustic detection and tracking," 2008 New Trends for Environmental Monitoring Using Passive Systems, Hyeres, French Riviera, 2008, pp. 1-5.doi: 10.1109/PASSIVE.2008.4786975
  20. Khudeev, "A new flood-fill algorithm for closed contour," 2005 Siberian Conference on Control and Communications, 2005, pp. 172-176. doi: 10.1109/SIBCON.2005.1611214
  21. Leymarie and M. D. Levine, "Simulating the grassfire transform using an active contour model," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 56-75, Jan 1992.
  22. Xiaoyin Xu, Jie Cheng, R. M. Witt, B. L. Sabatini and S. T. C. Wong, "A shape analysis method to detect dendritic spine in 3D optical microscopy image," 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006., Arlington, VA, 2006, pp. 554-557.
  23. Nasreddine and A. Benzinou, "Shape-based fish recognition via shape space,"2015 23rd European Signal Processing Conference (EUSIPCO), Nice, 2015, pp. 145-149.
  24. Matusugu, Masakazu; Katsuhiko Mori; Yusuke Mitari; Yuji Kaneda (2003). "Subject independent facial expression recognition with robust face detection using a convolutional neural network" (PDF). Neural Networks. 16 (5): 555?559.
  25. Kang, J. Kumar, P. Ye, Y. Li and D. Doermann, "Convolutional Neural Networks for Document Image Classification," 2014 22nd International Conference on Pattern Recognition, Stockholm, 2014, pp. 3168-3172.
  26. P. Sudharshan and S. Raj, "Object recognition in images using convolutional neural network,"?2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018, pp. 718-821.
  27. Matusugu, Masakazu; Katsuhiko Mori; Yusuke Mitari; Yuji Kaneda (2003). "Subject independent facial expression recognition with robust face detection using a convolutional neural network" (PDF). Neural Networks. 16 (5): 555?559.
  28. Kang, J. Kumar, P. Ye, Y. Li and D. Doermann, "Convolutional Neural Networks for Document Image Classification," 2014 22nd International Conference on Pattern Recognition, Stockholm, 2014, pp. 3168-3172.
  29. Muthugnanambika and S. Padmavathi, "Feature detection for color images using SURF," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, 2017, pp. 1-4.
  30. Pedersen, J.T. (2011). Study group SURF: Feature detection & description. Retrieved from: http://cs.au.dk/~jtp/SURF/report.pdf
  31. Wikipedia, ?Blob detection ? Wikipedia, the free encyclopedia,? 2011, [Online].Available: https://secure.wikimedia.org/wikipedia/en/wiki/Blob detection [Accessed April 29, 2018]
  32. B. David Lowe, ?Invariant features from interest point groups,? BMVC, 2002.
  33. Sledevič and A. Serackis, "SURF algorithm implementation on FPGA," 2012 13th Biennial Baltic Electronics Conference, Tallinn, 2012, pp. 291-294.

How to Cite This Article

M. I. Pavel, A. Akther, I. Chowdhury, S. A. Shuhin and J. Tajrin. (2019); DETECTION AND RECOGNITION OF BANGLADESHI FISHES USING SURF AND CONVOLUTIONAL NEURAL NETWORK., Int. J. of Adv. Res., 7 (06), 888-899, ISSN 2320-5407. DOI: https://doi.org/10.21474/IJAR01/9292

Corresponding Author

M. I. Pavel
BRAC University
Creative Commons Attribution 4.0 This work is licensed under a Creative Commons Attribution 4.0 International License.