Open Access
Issue |
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 | |
DOI | https://doi.org/10.1051/itmconf/20171203018 | |
Published online | 05 September 2017 |
- L. Itti. Visual salience. Scholarpedi., 2007, 2(9):3327. [CrossRef] [Google Scholar]
- 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] [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- 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] [Google Scholar]
- 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. [Google Scholar]
- 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] [Google Scholar]
- 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] [Google Scholar]
- 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. [Google Scholar]
- 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] [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- H. Peng, B . Li, W. Xiong, W. Hu, and R. Ji. RGBD Salient Object Detection: A Benchmark and Algorithms. In ECCV, 2014. [Google Scholar]
- A. Ciptadi, T. Hermans, and J. M. Rehg. An In Depth View of Saliency. In BMVC, 2013. [Google Scholar]
- K. Desingh, K. M. Krishna, D. Rajan, and C. V. Jawahar. Depth Really Matters: Improving Visual Salient Region Detection with Depth. In BMVC, 2013. [Google Scholar]
- 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] [Google Scholar]
- R. Ju, L. Ge, W. Geng, T. Ren, and G. Wu. Depth saliency based on anisotropic center-surround difference. ICIP, 2014. [Google Scholar]
- 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] [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- R. Margolin, A. Tal, L. Zelnik-Manor, What makes a patch distinct? IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1139–1146. [Google Scholar]
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.