한국수산과학회지 (Korean Journal of Fisheries and Aquatic Sciences)

  • 제50권2호
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  • Pages.195-206
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  • 2017
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  • 0374-8111(pISSN)
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  • 2287-8815(eISSN)
한국수산과학회 (The Korean Society of Fisheries and Aquatic Science)
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활어 개체어의 광대역 음향산란신호에 대한 시간-주파수 이미지의 어파인 변환과 주성분 분석을 이용한 어종식별

Identification of Fish Species using Affine Transformation and Principal Component Analysis of Time-Frequency Images of Broadband Acoustic Echoes from Individual Live Fish

  • 이대재 (부경대학교 해양생산시스템관리학부)
  • Lee, Dae-Jae (Division of Marine Production System Management, Pukyong National University)
  • 투고 : 2017.01.16
  • 심사 : 2017.02.20
  • 발행 : 2017.04.30
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초록

Joint time-frequency images of the broadband echo signals of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution in controlled environments. Affine transformation and principal component analysis were used to obtain eigenimages that provided species-specific acoustic features for each of the six fish species. The echo images of an unknown fish species, acquired in real time and in a fully automated fashion, were identified by finding the smallest Euclidean or Mahalanobis distance between each combination of weight matrices of the test image of the fish species to be identified and of the eigenimage classes of each of six fish species in the training set. The experimental results showed that the Mahalanobis classifier performed better than the Euclidean classifier in identifying both single- and mixed-species groups of all species assessed.

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참고문헌

  1. Abdullah M, Wazzan M and Bo-saeed S. 2012. Optimizing face recognition using PCA. Intern J Artific Intellig Applicat 3, 23-31. https://doi.org/10.5121/ijaia.2012.3303
  2. Alonso-Caneiro D, Read SA and Collins MJ. 2011. Speckle reduction in optical coherence tomography imaging by affinemotion image registration. J Biomed Opt 16, 1-5. http://dx.doi.org/10.1117/1.3652713.
  3. Arathi M and Govardhan A. 2014. Performance of Mahalanobis distance in time series classification using shapelets. Intern J Mach Leam Comput 4, 339-345. http://dx.doi.org/10.7763/IJMLC.2014.V4.434.
  4. Bhat FA and Wani MA. 2013. Improved face recognition algorithm using eigen faces. Internat J Advan Res Comp Sci Softw Engin 3, 824-828.
  5. Dong Y and Cui Y. 2012. Analysis of a new joint time-frequency distribution of suppressing cross-term. Res J Appl Sci Eng Technol 4, 1580-1584.
  6. Fassler SMM, Fernandes PG, Semple SIK and Brierley AS. 2009. Depthe-dependent swimbladder compression in herring Clupea haengus observed using magnetic resonance imaging. J Fish Bio 74, 296-303. http://dx.doi.org/10.1111/j.1095-8649.2008.02130.x.
  7. Foote KG. 1980. Importance of the swimbladder in acoustic scattering by fish: A Comparison of gadoid and mackerel target strengths. J Acoust Soc Am 67, 2084-2089. https://doi.org/10.1121/1.384452
  8. Fuh CS and Maragos P. 1991. Motion displacement estimation using affine model for image matching. Optical Engineering 30, 881-887. https://doi.org/10.1117/12.55885
  9. Gautam S. 2013. Face recognition using eigen faces and dimensionality reduction by PCA. Internat J Emerg Res Manage Technolo 2, 21-28.
  10. Gupta B and Singh AK. 2013. Analyzing face recognition using PCA and comparison between different distance classifier. Internat J Eenin Sci Res Technolo 2, 683-686.
  11. Jaffe JS. 2006. Using multi-angle scattered sound to size fish swimbladders. ICES J Mar Sci 63, 1397-1404. http://dx.doi.org/10.1016/j.icesjms.2006.04.024.
  12. Latha P, Ganesan L and Annadurai S. 2009. Face recognition using neural networks. Sign Process Internat J 3, 153-160.
  13. Lee DJ, Kang HY and Kwak MS. 2015. Analysis and classification of broadband acoustic echoes from individual live fish using the pulse compression technique. Korean J Fish Aquat Sci 48, 207-220. http://dx.doi.org/10.5657/KFAS.2015.0207.
  14. Lee DJ. 2015a. Time-frequency analysis of broadband acoustic scattering from chub mackerel Scomber japonicas, goldeye rockfish Sebestes thompsoni, and fat greenling Hexagrammos otakii. Korean J Fish Aquat Sci 48, 221-232. http://dx.doi.org/10.5657/KFAS.2015.0221.
  15. Lee DJ. 2015b. Changes in the orientation and frequency dependence of target strength due to morphological differences in the fish swim bladder. Korean J Fish Aquat Sci 48, 233-243. http://dx.doi.org/10.5657/KFAS.2015.0233.
  16. Lee DJ, Kang HY and Pak YY. 2016. Time-frequency feature extraction for species identification in broadband echo signals from individual live fish. Korean J Fish Aquat Sci 49, 214-223. http://dx.doi.org/10.5657/KFAS.2016.0214.
  17. Lee DJ. 2016. Acoustic identification of six fish species using artificial neural network. Korean J Fish Aquat Sci 49, 224-233. http://dx.doi.org/10.5657/KFAS.2016.0224.
  18. Nesse TL, Hobek H and Korneliussen RJ. 2009. Measurement of acoustic-scattering spectra from the whole and pars of Atlantic mackerel. ICES J Mar Sci 66, 1169-1175. http://dx.doi.org/10.1093/icesjms/fsp087.
  19. Prasad MSRS, Panda SS, Deepthi G and Anisha V. 2011. Face recognition using PCA and feed forward neural networks. Internat J Electron Communicat Technol 2, 79-82.
  20. Priscilla CV and Poorna B. 2013. Image registration and noise detection using affine transformation. Internat J Comput Technolo Applicat 4, 209-216.
  21. Rihaczek AW. 1968. Signal energy distribution in time and frequency. IEEE Trans Inform Theory IT-14, 369-374.
  22. Rizon M, Hashim MF, Saad P and Yaacob S. 2006. Face recognition using eigenfaces and neural networks. Ameri J. Appl Sci 2, 1872-1875.
  23. Semmlow JL. 2004. Biosignal and Biomedical Image Processing: Matlab-Based Applications, Marcel Dekker Inc, New York, USA, 320-327.
  24. Shui PL, Shang HY and Zhao YB. 2007. Instantaneous frequency estimation based on directionally smoothed pseudo-Wegner-Ville distribution bank. IET Radar Sonar Navig 1, 317-325. http://dx.doi.org/10.1049/rsn:20060123.
  25. Sijbers J, Scheunders P, Bonnet N, Van Dyck and Raman E. 1996. Quantification and improvement of the signal-tonoise ratio in a magnetic resonance image acquisition procedure. Magn Reson Imaging 14, 1157-1163. https://doi.org/10.1016/S0730-725X(96)00219-6
  26. Singla J and Goyal R. 2012. A systematic way of affine transformation using image registration. Internat J Infomat Technolo Knowledge Managm 5, 239-243.
  27. Slavkovic M and Jevtic D. 2012. Face recognition using eigenface approach. Serbian J Electri Engin 9, 121-130. https://doi.org/10.2298/SJEE1201121S
  28. Stanton TK, Chu D, Jech JM and Irish JD. 2010. New broadband methods for resonance classification and high-resolution imagery of fish with swimbladders using a modified commercial broadband echosounder. ICES J Mar Sci 67, 365-378. http://dx.doi.org/10.1093/icesjms/fsp262.