한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제21권1호
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  • Pages.123-129
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  • 2017
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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OpenCV를 이용한 안드로이드 플랫폼 기반 꽃 인식 시스템

Flower Recognition System Using OpenCV on Android Platform

  • Kim, Kangchul (Department of Computer Engineering, Gradute School, Chonnam National University) ;
  • Yu, Cao (Department of Computer Engineering, Gradute School, Chonnam National University)
  • 투고 : 2016.10.31
  • 심사 : 2016.11.09
  • 발행 : 2017.01.31
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초록

고성능 카메라와 고용량 메모리가 장착된 스마트폰이 출시되어 사람들은 야외나 산에서 촬영한 아름다운 경치, 꽃 등을 SNS에 많이 올리고 있다. 본 논문에서는 이동통신이 원활하지 않은 곳에서도 꽃의 정보를 얻을 수 있는 꽃 인식 시스템을 개발한다. 개발된 시스템은 OpenCV를 기반으로 안드로이드 스마트폰에서 사용할 수 있으며, 기준 꽃등록부와 꽃인식부로 구성된다. 인식처리 시간을 줄이기 위하여 RGB 색채널과 k-평균 클러스터링을 이용한 새로운 색분류방법을 제안하고, 특징 추출을 위하여 ORB, 매칭을 위하여 브루트포스 해밍 알고리즘을 사용한다. 4부류의 12 종류의 꽃에 대하여 60개의 이미지를 기준 DB 설계에 사용하고, 60개의 이미지를 테스트에 사용하였다. 모의 실험 결과 성공률은 83.3%이고, 화웨이 ALEUL100 스마트폰에서 평균인식 시간은 2.58 s 이며, 제안된 시스템은 통신이 되지 않는 곳에서도 휴대용 스마트폰에 적용가능하다는 것을 확인하였다.

New mobile phones with high tech-camera and a large size memory have been recently launched and people upload pictures of beautiful scenes or unknown flowers in SNS. This paper develops a flower recognition system that can get information on flowers in the place where mobile communication is not even available. It consists of a registration part for reference flowers and a recognition part based on OpenCV for Android platform. A new color classification method using RGB color channel and K-means clustering is proposed to reduce the recognition processing time. And ORB for feature extraction and Brute-Force Hamming algorithm for matching are used. We use 12 kinds of flowers with four color groups, and 60 images are applied for reference DB design and 60 images for test. Simulation results show that the success rate is 83.3% and the average recognition time is 2.58 s on Huawei ALEUL00 and the proposed system is suitable for a mobile phone without a network.

키워드

참고문헌

  1. S. W. Hong and L. Choi, "Automatic recognition of flowers through color and edge based contour detection," in Image Processing Theory, Tools and Applications (IPTA), 2012, 3rd International Conference, Oulu, pp.141-146, 15-18 Oct. 2012.
  2. E. Rublee, V. Rabaud, K. Konolige and G. Bradski. "ORB: An Efficient Alternative to SIFT or SURF," in IEEE International Conference on Computer Vision, Barcelona, pp. 2564-2571, Nov. 2011.
  3. H. Bay, A. Ess, and T. Tuytelaars. "Speeded-Up Robust Features (SURF)," in Computer Vision and Image Understanding, vol. 110, no. 4, pp. 346-359, Dec. 2008.
  4. E. Rosten and T. Drummond, "Machine learning for hign-speed corner detection," in 9th European Conference on Computer Vision, pp.430-443, UK, May 2006.
  5. M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "Brief: Bi nary robust independent elementary features," in European Conference on Computer Vision, Lausanne, pp.1-14, Sept. 2010.
  6. M. Muja, D. G. Lowe. "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration," in International Conference on Computer Vision Theory and Applications (VISAPP'09), Lisboa, pp.331-340, Jan. 2009.
  7. T. Saitoh, K. Aoki and T. Kaneko, "Automatic Recognition of Blooming Flowers," in The 17th International Conference on Pattern Recognition (ICPR'04), Washington, pp. 27-30, Sept. 2004.
  8. T. Saitoh and T. Kaneko, "Automatic Recognition of Wild Flowers", in The 15th International Conference on Pattern Recognition, Spain, pp. 507-510, Feb. 2000.
  9. M. Nilsback and A. Zisserman, "Automated Flower Classification over a Large Number of Classes," in The 6th Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), pp. 722-729, Bhubaneswar, Dec. 2008.
  10. T. Tiay, P. Benyaphaichit and P. Riyamongkol, "Flower Recognition System Based on Image Processing," in 2014 Third ICT International Student Project Conference, pp. 99-102, Salaya, Oct. 2014.
  11. J. H. Kim and R. G. Huang, "Mobile-Based Flower Recognition System," in 2009 Third International Symposium on Intelligent Information Technology Application, pp. 580-583, Nov. 2009.
  12. T. Sunpetchniyom, S. Watanapa and R. Siricharoenchai, "Flower search by image on mobile phone," in 2012 6th International conference on new trends in ISSDM, pp. 819-823, Taipei, Oct. 2012.
  13. I. Suryawibawa, I. Putra and N. Wirdiani, "Herbs Recognition Based on Android using OpenCV," in I.J. Image, Graphics and Signal Processing, Hong Kong, pp. 1-7, Jan. 2015.
  14. C. Pornpanomchai, P. Sakunreraratsame, R. Wongsasirinart, N. Youngtavichavhart, "Herb Flower Recognition System (HFRS)," in 2010 International Conference on Electronics and Information Engineering (ICEIE), pp.123-127, Kyoto, Aug. 2010.
  15. T. Tiay, P. Benyaphaichit and P. Riyamongkol, "Augmenting Flower Recognition by Automatically Expanding Training Data from Web," in Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop, Xiamen, 1-6, Sept. 2015.
  16. C. Rother, V. Kolmogorov, and A. Blake, "GrabCut: Interactive foreground extraction using iterated graph cuts," in ACM Transactions on Graphics, vol. 23, pp. 309-314, Aug. 2004.

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