한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제22권1호
  • /
  • Pages.15-21
  • /
  • 2017
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

Intelligent missing persons index system Implementation based on the OpenCV image processing and TensorFlow Deep-running Image Processing

  • Baek, Yeong-Tae (Dept. of Multimedia, Kimpo University) ;
  • Lee, Se-Hoon (Dept. of Computer Systems & Engineering, Inha Technical College) ;
  • Kim, Ji-Seong (Dept. of Computer Systems & Engineering, Inha Technical College)
  • 투고 : 2017.01.03
  • 심사 : 2017.01.20
  • 발행 : 2017.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we present a solution to the problems caused by using only text - based information as an index element when a commercialized missing person indexing system indexes missing persons registered in the database. The existing system could not be used for the missing persons inquiry because it could not formalize the image of the missing person registered together when registering the missing person. To solve these problems, we propose a method to extract the similarity of images by using OpenCV image processing and TensorFlow deep - running image processing, and to process images of missing persons to process them into meaningful information. In order to verify the indexing method used in this paper, we constructed a Web server that operates to provide the information that is most likely to be needed to users first, using the image provided in the non - regular environment of the same subject as the search element.

키워드

참고문헌

  1. Deock-Soon Son, "Establishment support system for missing children", Gyeonggi-do issue brief in the world, No. 2, April 2008.
  2. Hee-Seon Kim, "Proposal for Prevention of Mia and Support for Family Reunion", Politics in Korea, No. 162549, August 2003.
  3. won-seok Chae, "Face Detection Technology Trends, Electronics and Telecommunications Research Institute", 2013.02.15.
  4. OpenCV User Guide, "Cascade Classifier Training", 2007.
  5. Y. Bengio, A. Courville, and P. Vincent., "Representation Learning: A Review and New Perspectives," IEEE Trans. PAMI, special issue Learning Deep Architectures, 2013.
  6. J. Schmidhuber, "Deep Learning in Neural Networks: An Overview" http://arxiv.org/abs/1404.7828, 2014
  7. Logic Devices, "RGB to YCbCr Report" Logic Devices pp .2, Jan 2001.
  8. Intel , "Color Models" Intel Developer Zone , https://software.intel.com/en-us/node/503873
  9. Vietdungiitb, "Online handwriting recognition using multi convolution neural networks".
  10. Yeong-Tae Baek, Se-Hoon Lee, Ji-Seong Kim, "Gesture Recognition using binary processing and histogram graph with OpenCV", 2016.02.
  11. Vietdungiitb, "Online handwriting recognition using multi convolution neural networks".
  12. XiangWu, Ran He, Zhenan Sun, "A Lightened CNN for Deep Face Representation", 2015

피인용 문헌

  1. Detection of Moving Direction using PIR Sensors and Deep Learning Algorithm vol.24, pp.3, 2017, https://doi.org/10.9708/jksci.2019.24.03.011