Thinning based Antialiasing Approach for Visual Saliency of Digital Images

Olivier Rukundo

2015

Abstract

A thinning based approach for spatial antialiasing (TAA) has been proposed for visual saliency of digital images. This TAA approach is based on edge-matting and digital compositing strategies. Prior to edgematting the image edges are detected using ant colony optimization (ACO) algorithm and then thinned using a fast parallel algorithm. After the edge-matting, a composite image is created between the edge-matted and non-antialiasing image. Motivations for adopting the ACO and fast parallel algorithm in lieu of others found in the literature are also extensively addressed in this paper. Preliminary TAA experimental outcomes are more promising but with debatable smoothness to some extent of the original size of the images in comparison.

References

  1. Rukundo, O., Maharaj, B.T., 2014. Optimization of Image Interpolation Based on Nearest Neighbour Algorithm. In VISAPP'14, 9th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, pp. 641-647.
  2. Rukundo, O., Cao, H.Q., Huang, M.H., 2012, Optimization of Bilinear Interpolation Based on Ant Colony Algorithm, Lecture Notes in Electrical Engineering, Vol. 137, pp. 571-580.
  3. Rukundo, O., Wu, K.N., Cao, H.Q., 2011. Image Interpolation Based on the Pixel Value Corresponding to the Smallest Absolute Difference. In IWACI'11, 4th International Workshop on Computational Intelligence, Wuhan, China, pp. 432-435.
  4. Rukundo, O., Cao, H.Q., 2012. Nearest Neighbor Value Interpolation. International Journal of Advanced Computer Science and Applications. Vol. 3, No.4, pp. 25-30.
  5. Crow, F.C., 1981. A Comparison of Antialiasing Techniques. IEEE Computer Graphics and Applications, Vol. 1, pp. 40-48.
  6. Timor, K., Michael, B., 2001. Saliency, Scale and Image Description. International Journal of Computer Vision, Vol. 45, No. 2, pp. 83-105.
  7. Tong,Y.B., Cheikh,F.A.,Konik, H., Tremeau A., 2010. Full reference image quality assessment based on saliency map analysis. International Journal of Imaging Science and Technology, Vol. 54, No.3, pp. 030503-030514.
  8. Itti, L., Koch, C., Niebur, E., 1998. A Model of SaliencyBased Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, pp.1254-1259.
  9. Tong, Y. B., Cheikh, F. A., Guraya, F.F.E., Konik, H., Tremeau, A., 2011. A Spatiotemporal Saliency Model for Video Surveillance. Journal of Cognitive Computing, Vol.3, No.1, pp.241-263.
  10. Bloomenthal, J., 1983. Edge Inference with Applications to Antialiasing. In SIGGRAPH 7883, 10th Annual Conference Computer Graphics and Interactive Techniques, Detroit, USA, pp. 157-162.
  11. Iourcha, K., Yang, J.C., Pomianowski, A., 2009. A directionally adaptive edge anti-aliasing filter. In HPG 7809, Conference on High Performance Graphics, New Orleans, Louisiana, pp. 127-133.
  12. Dorigo, M., Stutzle, T., 2004. Ant Colony Optimization. MIT Press, Massachusetts. Illustrated Ed.
  13. Baterina, A.V., Oppus, C., 2010. Image Edge Detection Using Ant Colony Optimization. WSEAS Transactions on Signal Processing, Vol. 6, pp. 58-67.
  14. Rezaee, A., 2008. Extracting Edge of Images with Ant Colony. Journal of Electrical Engineering. Vol. 59, pp. 57-9.
  15. Lu, D.S., Chen, C.C., 2008. Edge Detection Improvement by Ant Colony Optimization. Pattern Recognition Letters. Vol. 29, pp. 416-25.
  16. Canny, J., 1986. A Computational Approach to Edge Detection. IEEE Trans. Pattern Analysis and Machine Intelligence. Vol. 8, pp. 679-698.
  17. Gonzalez, R. C., Woods, R. E., 2008. Digital Image Processing. Pearson Prentice Hall, 3rd Edition.
  18. Abe, K., Mizutani, F., Wang, C.H., 1994. Thinning of Gray-scale Images with Combined Sequential and Parallel Conditions for Pixel Removal. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 24, pp. 294-299.
  19. Wang, P.S.P., Zhang, Y.Y., 1989. A Fast and Flexible Thinning Algorithm. IEEE Transactions on Computers. Vol. 38, pp. 741-744.
  20. Zhang, T. Y., Suen, C. Y., 1984. A Fast Parallel Algorithm for Thinning Digital Patterns. Communication of the ACM. Vol. 27, pp. 236-239.
  21. Szeliski, R., 2010. Computer Vision: Algorithms and Applications. Springer London, Illustrated Ed.
  22. Porter, T., Duff, T., 1984. Compositing Digital Images. ACM SIGGRAPH Computer Graphics. Vol. 18, pp. 253-259.
  23. Tian, J., Yu, W.Y., Xie, S.L., 2008. An Ant Colony Optimization Algorithm for Image Edge Detection. In CEC'08, Congress on Evolutionary Computation, Hong Kong, pp. 751-756.
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Paper Citation


in Harvard Style

Rukundo O. (2015). Thinning based Antialiasing Approach for Visual Saliency of Digital Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 658-665. DOI: 10.5220/0005356206580665


in Bibtex Style

@conference{visapp15,
author={Olivier Rukundo},
title={Thinning based Antialiasing Approach for Visual Saliency of Digital Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={658-665},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005356206580665},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Thinning based Antialiasing Approach for Visual Saliency of Digital Images
SN - 978-989-758-089-5
AU - Rukundo O.
PY - 2015
SP - 658
EP - 665
DO - 10.5220/0005356206580665