KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Backlight Compensation by Using a Novel Region of Interest Extraction Method

새로운 관심영역 추출 방법을 이용한 역광보정

  • 성준모 (한국교통대학교 컴퓨터정보공학과) ;
  • 이성신 (한국교통대학교 컴퓨터정보공학과) ;
  • 이성욱 (한국교통대학교 컴퓨터정보공학과)
  • Received : 2016.12.16
  • Accepted : 2017.02.24
  • Published : 2017.06.30
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Abstract

We have implemented a technique to correct the brightness, saturation, and contrast of an image according to the degree of light, and further compensate the backlight. Backlight compensation can be done automatically or manually. For manual backlight compensation, we have to select the region of interest (ROI). ROI can be selected by connecting the outline of the desired object. We make users select the region delicately with the new magnetic lasso tool. The previous lasso tool has a disadvantage that the start point and the end point must be connected. However, the proposed lasso tool has the advantage of selecting the region of interest without connecting the start point and the end point. We can automatically obtain various results of backlight compensation by adjusting the number of k-means clusters for texture extraction and the threshold value for binarization.

우리는 빛의 정도에 따라 이미지의 밝기와 채도, 대비를 보정하고 더 나아가 역광을 보정하는 기술을 구현하였다. 역광보정은 자동이나 수동으로 할 수 있는데, 수동으로 역광보정을 적용하기 위해서는 먼저 관심영역을 지정해 주어야 한다. 관심영역은 사진 속 원하는 사물의 윤곽선을 이어줌으로써 선택한다. 우리는 자석 올가미를 이용하여 사용자가 섬세한 선택을 가능하게 하였다. 기존 올가미 기능은 시작점과 끝점을 일치시켜 주어야 하는 단점이 있었으나 제안하는 올가미 기능은 시작점과 끝점을 일치시키지 않아도 관심영역을 선택할 수 있는 장점이 있다. 또한 사용자가 이진화 임계값과 질감추출을 위한 k-means 군집의 개수를 선택할 수 있도록 하여 다양한 역광보정 결과를 자동으로 얻을 수 있게 하였다.

Keywords

References

  1. Dae-Geun Park, Kee-Hyon Park, Oh-Seol Kwon, and Yeong-Ho Ha, "Acquisition of efficient HDR image using estimation of dynamic range of scene in camera images with various exposures," Proceedings of IEEK Summer Conference, pp.233-234, 2007.
  2. Dae-Young Hyun, Jun-Hee Heu, Chang-Su Kim, and Sang- Uk Lee, "Region-based backlight compensation algorithm for images and videos," IEEE(ICIP) Conference Publications, pp.3545-3546, 2010.
  3. Lucas Bastos, Aura Conci, and P. Liatsis, "Automatic Texture Segmentation based on k-means Clustering and Co-occurrence Features," Proceedings of International Conference on Systems, Signal and Image Processing, pp.141-144, 2008.
  4. Eric N. Mortensen, L. Jack Reese, and William A. Barrett, "Intelligent Selection Tools," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 776-777, 2000.
  5. Yeong-Geon Seo, Hee-Min Kim, and Sang-Bok Kim, "A Generation of ROI Mask and An Automatic Extraction of ROI Using Edge Distribution of JPEG2000 Image," Journal of Digital Contents Society, Vol.16, No.4, pp.583-593, 2015. https://doi.org/10.9728/dcs.2015.16.4.583
  6. Jun-Gyn Park, Hye-Kyoung Jang, and Dae-Seong Kang, "Region of Interest (ROI) Studies Using a Sophisticated Face Recognition Algorithm," Proceedings of KIIT Summer Conference, pp.116-119, 2014.
  7. Chiun-Li Chin and Chin-Teng Lin, "Detection and Compensation Algorithm for Backlight Images with Fuzzy Logic and Adaptive Compensation Curve," International Journal of Pattern Recognition and Artificial Intelligence, Vol.19, No.8, pp.1041-1057, 2005. https://doi.org/10.1142/S0218001405004411
  8. C. J. Lin and Y. C. Liu, "Image backlight compensation using neuro-fuzzy networks with immune particle swarm optimization," Expert Systems with Applications, Vol.36, No.3, pp.5212-5220, 2009. https://doi.org/10.1016/j.eswa.2008.06.109
  9. Jiazhong Chen, Bingpeng Ma, Rong Li, Tao Xia, and Hua Cao, "Image Dimming Perceptual Model Based Pixel Compensation and Backlight Adjustment," Journal of Display Technology, Vol.11, No.9, pp.744-752, 2015. https://doi.org/10.1109/JDT.2015.2436403
  10. Young-Tak Kim, Jae-Hyoung Yu, and Hern-Soo Hahn, "Retinex Algorithm Improvement for Color Compensation in Back-Light Image Efficently," Journal of the Korea Society of Computer and Information, Vol.16, pp.61-69, 2011.
  11. Eric N. Mortensen, William A. Barrett, "Intelligent Scissors for Image Composition," Proceedings of the 22nd annual conference on Computer Graphics and Interactive Techniques, pp.192-194, 1995.
  12. John Canny, "A Computational Approach to Edge Detection," IEEE Trans, Pattern Analysis and Machine Intelligence, Vol.PAMI-8, No.6, pp.679-697, 1986. https://doi.org/10.1109/TPAMI.1986.4767851
  13. E. W. Dijkstra, "A Note on Two Problems in Connexion with Graphs," Numerische Mathematick 1, pp.269-271, 1959. https://doi.org/10.1007/BF01386390
  14. D. Walther, "Interactions of Visual Attention and Object Recognition: Computational Modeling, Algorithms, and Psychophysics," Dissertation, California Institute of Technology, 2006.