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A Video Retargeting Technique for RGB-D Camera

  • Huei-Yung LinEmail author
  • Chin-Chen Chang
  • Jhih-Yong Huang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 458)

Abstract

This paper presents a content aware video retargeting technique with the help of an RGB-D camera based on the detection of saliency objects. The content aware image resizing algorithm requires some energy terms to separate the main contents and the background. In this work, we use the scene depth information, gradient information, visual saliency and saliency object to create an image on the visual focus of the energy map. The experimental results show that the proposed approach performs well in terms of the resized quality.

Keywords

Image retargeting RGB-D Camera Feature map Depth map 

References

  1. 1.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. 26(3), 10 (2007)CrossRefGoogle Scholar
  2. 2.
    Hwang, D.S., Chien, S.Y.: Content-aware image resizing using perceptual seam carving with human attention model. In: 2008 IEEE International Conference on Multimedia and Expo, pp. 1029–1032, 23–26 April 2008Google Scholar
  3. 3.
    Kim, J., Kim, J., Kim, C.: Image and video retargeting using adaptive scaling function. In: Proceedings of 17th European Signal Processing Conference (2009)Google Scholar
  4. 4.
    Kim, J.S., Kim, J.H., Kim, C.S.: Adaptive image and video retargeting technique based on fourier analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1730–1737, June 2009Google Scholar
  5. 5.
    Wang, Y.S., Tai, C.L., Sorkine, O., Lee, T.Y.: Optimized scale-and-stretch for image resizing. In: SIGGRAPH Asia ’08: ACM SIGGRAPH Asia 2008 papers, pp. 1–8. ACM, New York (2008)Google Scholar
  6. 6.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-264–II-271, June 2003Google Scholar
  7. 7.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  8. 8.
    Gao, D., Vasconcelos, N.: Integrated learning of saliency, complex features, and object detectors from cluttered scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 282–287, June 2005Google Scholar
  9. 9.
    Liu, T., Sun, J., Zheng, N. N., Tang, X., Shum, H. Y.: Learning to detect a salient object. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’07, pp. 1–8, June 2007Google Scholar
  10. 10.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Netw. 19(9), 1395–1407 (2006). Brain and AttentionCrossRefzbMATHGoogle Scholar
  11. 11.
    Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’07, pp. 1–8, June 2007Google Scholar
  12. 12.
    Rubinstein, M., Shamir, A., Avidan, S.: Improved seam carving for video retargeting. ACM Trans. Graph. 27(3), 1–9 (2008)CrossRefGoogle Scholar
  13. 13.
    Wang, L., Jin, H., Yang, R., Gong, M.: Stereoscopic inpainting: Joint color and depth completion from stereo images, pp. 1–8, June 2008Google Scholar
  14. 14.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597–1604, June 2009Google Scholar
  15. 15.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2376–2383, June 2010Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Huei-Yung Lin
    • 1
    Email author
  • Chin-Chen Chang
    • 2
  • Jhih-Yong Huang
    • 1
  1. 1.Department of Electrical EngineeringNational Chung Cheng UniversityChiayiTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational United UniversityMiaoliTaiwan

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