Open Access
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 03018
Number of page(s) 6
Section Session 3: Computer
Published online 05 September 2017
  1. L. Itti. Visual salience. Scholarpedi., 2007, 2(9):3327. [CrossRef]
  2. Y. Fang, Z. Wang, W. Lin, and Z. Fang. Video saliency incorporating spatiotemporal cues and uncertainly weighting. IEEE Trans. Image Process., 2014, 23(9): 3910–3921. [CrossRef] [MathSciNet]
  3. M. M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu. Global contrast based salient region detection. in Prol. IEEE Conf. Comput. Vis. Pattern Recog (CVPR)., 2011, pp. 409–416.
  4. L. Itti, C. Koch, and E. Niebur. A model for saliencybased visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Tntell., 1998, 20(11): 1254–1259. [CrossRef]
  5. L. Zhou, Z. Yang, Q. Yuan, Z. Zhou, and D. Hu. Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans. Image Process., 2015, 24(11): 3308–3320. [CrossRef] [MathSciNet]
  6. M. M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S. M. Hu. Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37(3): 569–582. [CrossRef]
  7. J. Sun, H. Lu, and X. Liu. Saliency region detection based on Markov absorption probabilities. IEEE Trans. Image Process. Lett., 2015, 24(5): 1639–1649. [CrossRef]
  8. X. Wei, Z. Tao, C. Zhang, and X. Cao. Structured saliency fusion based on Dempster-Shafer theory. IEEE Signal Process. Lett., 2015,22(9): 1345–1349. [CrossRef]
  9. C. Yang, L. Zhang, H. Lu, X. Ruan, and M. H. Yang. Saliency detection via graph-based manifold ranking. in Proc. IEEE Conf. Comput. Vis. Pattern Recog . (CVPR), 2013, pp. 3166–3173.
  10. Y. Fang, J. Wang, M. Narwaria, P. Le Callet, and W. Lin. Saliency detection for stereoscopic images. IEEE Trans. Image Process., 2014, 23(6): 2625–2636. [CrossRef] [MathSciNet]
  11. C. Lang, T. V. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, and S. Yan. Depth Matters: Influence of Depth Cues on Visual Saliency. In ECCV, 2012.
  12. J. Ren, X. Gong, L. Yu, W. Zhou, and M. Yang. Exploiting global priors for RGB-D Saliency detection. In CVPRW, 2015, pp. 25–32.
  13. H. Peng, B . Li, W. Xiong, W. Hu, and R. Ji. RGBD Salient Object Detection: A Benchmark and Algorithms. In ECCV, 2014.
  14. A. Ciptadi, T. Hermans, and J. M. Rehg. An In Depth View of Saliency. In BMVC, 2013.
  15. K. Desingh, K. M. Krishna, D. Rajan, and C. V. Jawahar. Depth Really Matters: Improving Visual Salient Region Detection with Depth. In BMVC, 2013.
  16. Yuzhen Niu, Yujie Geng, Xueqing Li, and Feng Liu. Leveraging stereopsis for saliency analysis. in IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012, pp. 454–461. [EDP Sciences]
  17. R. Ju, L. Ge, W. Geng, T. Ren, and G. Wu. Depth saliency based on anisotropic center-surround difference. ICIP, 2014.
  18. R. Ju, Y. Liu, T. Ren, L. Ge, and G. Wu. Depth-aware salient object detection using anisotropic center surround difference. Signal Process. Image Commun., 2015, 38(10): 115–126. [CrossRef] [EDP Sciences]
  19. Deqing Sun, Stefan Roth, and Michael J Black. Secrects of optical flow estimation and their principles. in IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2010, pp. 2432–2439.
  20. Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence., 2012, 34(10):2274–2282. [CrossRef]
  21. R. Margolin, A. Tal, L. Zelnik-Manor, What makes a patch distinct? IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1139–1146.

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.