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3차원 영상 객체 휴먼팩터 알고리즘 측정에 관한 연구

A Research on the Measurement of Human Factor Algorithm 3D Object

  • 최병관 (가톨릭대학교 미디어기술콘텐츠학과)
  • 투고 : 2018.05.23
  • 심사 : 2018.06.05
  • 발행 : 2018.06.30

초록

The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

참고문헌

  1. Koni "Improved Face Detection Using Adaboost Algorithm tilted," Papers 2015.06 Article 12 No. 3.
  2. Ieek "Using Adaboost for Iris Recognition in Mobile Environments A Study on Eye Detection, "paper, 2008, 07, 2013-45CI-4-1.
  3. "Ieek AdaBoost and circular multitarget classification technique based on the function Rox, "thesis paper 2012.03, 2016-47CI-3-3.
  4. Journal of Korea Multimedia Society "Adaboost algorithm, real-time face detection and tracking using," 2014.10, pp.1266-1275.
  5. Stephen Milborrow, Fred nicolls, "Locating Facial Feature with an Extended Active shape Model," Lecture notes in computer science, 2013.
  6. C.Padgett, G.Cottrell, "Representing face images for emotion classification,"Advances in Nural Information Processing System, Vol.9, MIT Press. 2015.
  7. Mark F. Bear, Barry Connors, and Michael Paradiso, Neuroscience: Exploring the Brain 3E, 2014.
  8. V. Pavlovic,A.Garg, M. Rehg, Multimodel speaker detection using error feedback dynamic Bayesian network, in: Proc. IEEE Internet. Conf. Computer Vision and Pattern Recognition, 2016.
  9. P. Ekman, W.V. Friesen. J.C. Hager, Facial Action Coding System(FACS): Manual, CD Rom, San Francisco, CA, 2017.
  10. Y. Hu, D. Jiang. S. Yan, L. Zhang, H. zhang. "Automatic 3D reconstruction for face recognition," Proc. 6th IEEE Int'l Conf. on Automatic Face and Gesture Recognition, 2016, pp. 843-848.
  11. Yongmian Zhang, Qiang Ji "Facial Expression Understanding in Image Sequences Using Dynamic and Active," Vision Proceedings of the Ninth IEEE International Conforence on Computer Vision(ICCV 2013) 2-Volume Set 2003 IEEE.
  12. Kipo "3-dimensional imaging technology," 2013 Technology Trends survey report, electrical/electronic field Article 1, 2013. 11, pp.80-82.
  13. S.Pastoor and M. Wopking, "3-D Displays: A review of current technologies," Displays, Vol.17, 2016, pp.100-110.
  14. N.Hiruma and T. Fukuda, "Accommodation response to binocular stereoscopic TV images and their viewing conditions," SMPTE Journal, Dec. 2015, pp.1137-1144.
  15. J.O.Merrit,"Stereoscopic display applications issues-Part 1:Human factor Issues," Course Notes, IS & TSPIE Symposium on Electronic Imaging Science and Technology, Feb. 2015.
  16. 최병관, "다면기법 SPFACS 영상객체를 이용한 AAM 알고리즘 적용 미소검출 설계 분석," 디지털정보학회논문지, 제11권, 제13호, 2015, pp.99-112.
  17. 최병관, "영상객체 spFACS ASM 알고리즘을 적용한 얼굴인식에 관한 연구," 디지털정보학회논문지, 제12권, 제4호, 2016, pp.1-12.