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Object transparent vision combining multiple images from different views

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Abstract

Augmented reality (AR) has been studied extensively and used widely in various fields. In AR a view of reality is modified or supplemented by computer-gene-rated sensory input such as sound, video, etc. As a result, it can enhance one’s current perception of reality. In this paper we analyzed methods to make objects transparent by fusing images taken from two different cameras. Induced transparency could be particularly interesting for traffic applications. When two cars are driving one after another and the car in front can block the view for the second car, however, if both cars are equipped with cameras capturing the front view, their images can be merged and the front car can be made transparent allowing driver to have clear picture about obstacles and other dangers on the road. The goal of this paper is to analyze various solutions that can provide such transparency effect and also to create a solution that can work in real-time.

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References

  1. Bailenson, J.N. and Blascovich, J., Virtual reality and social networks will be a powerful combination, IEEE Spectrum, 2011. http://spectrum.ieee.org/telecom/internet/virtual-reality-and-social-networks-will-be-apowerful-combination

    Google Scholar 

  2. Google Glass. http://www.google.com/glass/start/

  3. Rolland, J.P., and Thompson, K.P., See-through head worn displays for mobile augmented reality, Proc. China National Computer Conference (CNC 2011), 2011.

    Google Scholar 

  4. Argelaguet, F., Kulik, A., Kunert, A., Andujar, C., and Froehlich, B., See-through techniques for referential awareness in collaborative virtual reality, Int. J. Hum.-Comput. Stud., 2011, no. 69(6), p. 387–400.

    Article  Google Scholar 

  5. Buchmann, V., Nilsen, T., and Billinghurst, M., Interaction with partially transparent hands and objects, Proc. 6th Australasian User Interface Conference (AUIC 2005), pp. 17–20.

    Google Scholar 

  6. Wigdor, D., Forlines, C., Baudisch, P., Barnwell, J., and Shen, C., LucidTouch: A see-through mobile device, Proc. 20th annual ACM symposium on User Interface Software and Technology (UIST 2007), pp. 269–278.

    Chapter  Google Scholar 

  7. Land Rover’s Transparent Bonnet is part of a ‘New Age of Discovery’. http://thegadgetflow.com/blog/land-roverstransparent-bonnet-part-new-age-discovery/

  8. Gomes, P., Vieira, F., and Ferreira, M., The see-through system: From implementation to test-drive, Proc. 2012 IEEE Vehicular Networking Conference (VNC 2012).

    Google Scholar 

  9. OpenCV. http://opencv.org

  10. Emgu CV: OpenCV in. NET. http://www.emgu.com/wiki/index.php/Main_Page

  11. Canny, J., A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell., 1986, no. 8(6), pp. 679–698.

    Article  Google Scholar 

  12. Suzuki, S. and Abe, K., Topological structural analysis of digitized binary images by border following, Comput. Vision Gr. Image Process., 1985, no. 30(1), pp. 32–46.

    Article  MATH  Google Scholar 

  13. Viola, P. and Jones, M., Rapid object detection using a boosted cascade of simple features, Conference on Computer Vision and Pattern Recognition (CVPR), 2001, pp. 511–518.

    Google Scholar 

  14. Viola, P. and Jones, M., Robust real-time face detection, Int. J. Comput. Vision, 2004, no. 57(2), pp. 137–154.

    Article  Google Scholar 

  15. Tutorial OpenCV. http://note.sonots.com/SciSoftware/haartraining.html

  16. Bay, H., Tuytelaars, T., and Van Gool, L., Surf: Speeded up robust features, Proc. European Conference on Computer Vision (ECCV 2006), pp. 404–417.

    Google Scholar 

  17. Lowe, D.G., Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, 2004, no. 60(2), pp. 91–110.

    Article  Google Scholar 

  18. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G., ORB: An efficient alternative to SIFT or SURF, Int. Conf. on Computer Vision (ICCV 2011), pp. 2564–2571.

    Google Scholar 

  19. Camera Calibration and 3D reconstruction. http://docs.opencv.org/modules/calib3d/doc/camera_calibration_ and_3d_reconstruction.html#Mat findHomography(InputArray srcPoints, InputArray dstPoints, int method, double ransacReprojThreshold, OutputArray mask)

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Correspondence to J. Judvaitis.

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Judvaitis, J., Hermanis, A., Nesenbergs, K. et al. Object transparent vision combining multiple images from different views. Aut. Control Comp. Sci. 49, 313–320 (2015). https://doi.org/10.3103/S0146411615050053

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  • DOI: https://doi.org/10.3103/S0146411615050053

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