Journal of Information Technology and Architecture (정보화연구)

Korea Institute of Enterprise Architecture (한국엔터프라이즈아키텍처학회)
권호 표지

Auto Correction Technique of Photography Composition Using ROI Extraction Method

ROI 추출을 통한 사진 구도 자동 보정 기법

  • Ha, Ho-Saeng (Department of Computer and Communication Engineering, Kangwon National University) ;
  • Park, Dae-Hyun (Department of Computer and Communication Engineering, Kangwon National University) ;
  • Kim, Yoon (Department of Computer and Communication Engineering, Kangwon National University)
  • 하호생 (강원대학교 컴퓨터정보통신공학과) ;
  • 박대현 (강원대학교 컴퓨터정보통신공학과) ;
  • 김윤 (강원대학교 컴퓨터정보통신공학과)
  • Published : 2013.03.30
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Abstract

In this paper, we propose the method that automatically corrects the composition of a picture stylishly as well as reliably by cropping pictures based on the Rule of Thirds. The region of interest (ROI) is extracted from a picture by applying the Saliency Map and the Image Segmentation technology, the composition of the photo is amended based on this area to satisfy the Rule of Thirds. In addition, since the face region of the person is added to ROI by the Face Detection technique and the composition is amended by the various scenario according to ROI, the little more natural picture is acquired. The experimental result shows that the photo of the corrected composition was naturally amended compared with the original photo.

본 논문에서는 영상을 3분할 기법에 맞춰 재구성함으로써 자동으로 구도를 안정적이고 세련되게 보정하는 기법을 제안한다. Saliency Map과 Image Segmentation기술을 이용하여 사진에서 피사체의 관심영역(Region Of Interest, ROI)을 구하고, 그 영역을 기준으로 3분할 기법에 맞도록 사진을 Cropping하여 구도를 보정한다. 또한, 얼굴 인식(Face Detection)기법을 활용하여 사람의 얼굴을 ROI에 추가하고 ROI에 따른 다양한 시나리오에 의하여 구도를 보정함으로써, 좀 더 자연스러운 사진을 얻는다. 실험결과를 통해 보정된 구도의 사진이 원본사진과 비교하여 자연스럽게 보정이 되었는다는 것을 알 수 있다.

Keywords

References

  1. Craw, I., Tock, D., and Bennett, A., "Finding face features," Proc.of 2nd European Conf. Computer Vision. pp. 92-96, 1992.
  2. Lanitis, A., Taylor, C. J., and Cootes, T. F., "An automatic face identification system using flexible appearance models," Image and Vision Computing, Vol. 13, No. 5, pp. 393-401, 1995. https://doi.org/10.1016/0262-8856(95)99726-H
  3. Leung, T. K., Burl, M. C., and Perona, P., "Finding faces in cluttered scenes using random labeled graph matching," Proc. 5th IEEE int''l Conf. Computer Vision, pp. 637-644, 1995.
  4. Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., and Hu, S.-M., "Global contrast based salient region detection," In CVPR, pp. 409-416, 2011.
  5. Hou, X., and Zhang, L., "Saliency detection: A spectral residual approach," In CVPR, 2007.
  6. Harel, J., Koch, C., and Perona, P., "Graph-based visual saliency," Advances in Neural Information Processing Systems, pp. 545-552, 2007.
  7. Ma, Y.-F., and Zhang, H.-J., "Contrast-based image attention analysis by using fuzzy growing," In ACM International Conference on Multimedia, 2003.
  8. Hou, X., and Zhang, L., "Saliency detection: A spectral residual approach," IEEE Conference on Computer Vision and Pattern Recognition, 2007.
  9. Caroline Pantofaru and Martial Hebert. "A Comparison of Image Segmentation Algorithms," 2005.
  10. Wang, J., and Cohen, M. F., "An iterative optimization approach for unified image segmentation and matting," In Proceedings of IEEE ICCV 2005, pp. 936-943, 2005.
  11. Vance Faber, "Clustering and Continuous k-Means Algorithm," Los Alamos Science Number 22, 1994.
  12. Kim, J. W., Cho, Y. S., and Kang, S. G., "Natural Scene Image Categorization using Structure of Location of Semantic Concepts," 한국EA학회, 정보기술아키텍처연구, 7권, 1호, 2010.
  13. Min, M. K., "Study on Strategies for Integration of Multimedia Digital Libraries," 한국EA학회, 정보기술아키텍처연구, 5권, 1호. 2008.