전기전자학회논문지 (Journal of IKEEE)

  • 제22권2호
  • /
  • Pages.350-355
  • /
  • 2018
  • /
  • 1226-7244(pISSN)
  • /
  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
권호 표지
DOI QR코드

DOI QR Code

기준점과 크기를 사용한 객체 인식 시스템 향상

Enhanced Object Recognition System using Reference Point and Size

  • Lee, Taehwan (Dept. of Electronic Engineering, Sangmyung University) ;
  • Rhee, Eugene (Dept. of Electronic Engineering, Sangmyung University)
  • 투고 : 2018.06.11
  • 심사 : 2018.06.29
  • 발행 : 2018.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 영상 내에서의 객체를 기준점을 사용하여 크기에 따라 분류할 수 있는 시스템을 제안한다. 본 논문에선 객체를 샘플로 하여 연구를 진행하였다. 제안된 시스템은 휴대폰 카메라를 이용하여 획득한 영상에서 객체를 크기 별로 인식해서 그 종류를 파악하고 분류한다. 기존의 객체 인식 시스템들은 객체의 크기만을 이용해서 해당 객체를 분류하였다. 그러한 시스템들은 일정한 거리를 두어 획득한 영상이 아니면 거리에 따라 객체의 크기가 달라져 오류가 발생하는 단점이 있다. 이에 본 논문에서 제안하는 객체 인식 시스템은 이러한 기존의 객체 인식 시스템의 한계를 극복하고자 영상의 왼쪽 상단에 기준점을 두어 그 기준점과 객체의 크기를 비교하여 거리에 상관없이 객체를 분류할 수 있다.

In this paper, a system that can classify the objects in the image according to their sizes using the reference points is proposed. The object is studied with samples. The proposed system recognizes and classifies objects by the size in images acquired using a mobile phone camera. Conventional object recognition systems classify objects using only object size. As the size of the object varies depending on the distance, such systems have the disadvantage that an error may occurs if the image is not acquired with a certain distance. In order to overcome the limitation of the conventional object recognition system, the object recognition system proposed in this paper can classify the object regardless of the distance with comparing the size of the reference point by placing it at the upper left corner of the image.

키워드

참고문헌

  1. H. Won and K. Lee. "Fast Hough Circle Detection using Motion in Video Frames," Journal of Korean Society for Internet Information, Vol. 11, No. 6, pp. 31-39, 2010.
  2. S. Chae and K. Jun, "Automatic Coin Calculation System using Circular Hough Transform and Post-processing Techniques," Journal of Korea Multimedia Society, Vol. 17, No. 4, pp. 413-419, 2014. DOI: 10.9717/kmms.2014.17.4.413
  3. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-basaed Object Tracking," IEEE Trans. Patt. Analy. Mach. Intel, Vol. 25, pp. 564-575, 2003. DOI: 10.1109/TPAMI.2003.1195991
  4. S. Chae and K. Jun, "Automatic Coin Calculation System using Circular Hough Transform and Post-processing Techniques," Journal of Korea Multimedia Society, Vol. 17, No, pp. 413-419, 2014. DOI : 10.9717/kmms.2014.17.4.413
  5. J. Lee, J. Lee, and C. Hyun, "Coin Recognition and Classification using Digital Image Processing." Journal of Korean Institute of Intelligent Systems, Vol. 22, No. 1, pp. 7-11, 2012. DOI : 10.5391/JKIIS.2012.22.1.7
  6. J. Choi and C. Kim, "Interval Hough Transform For Prominent Line Detection," Journal of Korea Multimedia Society, Vol. 16, No. 11, pp. 1288-1296, 2013. DOI : 10.9717/kmms.2013.16.11.1288
  7. P. M. Merlin and D. J. Farber, "A Parallel Mechanism for Detecting Curves in Pictures," IEEE Trans. Computer, Vol. 24, pp. 96-98, 1975. DOI: 10.1109/T-C.1975.224087
  8. S. Malik, P. Bajaj and M. Kaur, "Sample Coin Recognition System using Artificial Neural Network on Static Image Dataset Network on Static Image Dataset," International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 1, pp. 762-770, 2014
  9. T. Kailath, "The Divergence and Bhattacharyya Distance Measures in Signal Selection," IEEE Trans. Communication Technology, Vol. 15, No. 1, pp. 52-60, 1996. DOI: 10.1109/TCOM.1967.1089532
  10. J. Shi and C. Tomasi, "Good Features to Track," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 593-600, 1994. DOI: 10.1109/CVPR.1994.323794
  11. S. Avidan, "Support Vector Tracking," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 184-191, 2001. DOI: 10.1109/CVPR.2001.990474
  12. J. Lym, Y. Lee, S. Moon, and S. Yang, "Detection of Circle and Rectangle Image by Hough Transform for a Tape Substrate Alignment," Journal of Institute of Control, Robotics and Systems, Vol. 37, pp. 140-144, 2008.
  13. J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, 1986. DOI: 10.1109/TPAMI.1986.4767851