Saliency Map for Visual Attention Region Prediction: A Comparison of Two Methods
release_lskesidzkrgxhkwqjqulaopmyy
by
Mao Wang
References
NOTE: currently batch computed and may include additional references sources, or be missing recent changes, compared to entity reference list.Showing 1 - 16 of 16 references (in 89ms) | ||
---|---|---|
[b0] via grobid |
F. Sadri, Logic-Based Approaches to Intention Recognition, Handbook of Research on Ambient Intelligence: Trends and Perspectives, 2010.
| |
[b1] via fuzzy |
Accounting for Context in Plan Recognition, with Application to Traffic
Monitoring
[report]
David V. Pynadath, Michael P. Wellman 2013 pre-print version:v1 number:UAI-P-1995-PG-472-481 arXiv:1302.4980v1 |
web.archive.org [PDF]
|
[b2] via grobid |
L. M. Pereira and H. T. Anh, Intention Recognition via Causal Bayes Networks Plus Plan Generation, Progress in Artificial Intelligence. (2009) pp. 138-149.
| |
[b3] via grobid |
K. A. Tahboub, Intelligent Human-Machine Interaction Based on Dynamic Bayesian Networks Probabilistic Intention Recognition, Journal of Intelligent and Robotic Systems. Vol.45(2006), pp.31-52.
| |
[b4] via grobid |
J. W. Harris and H. Stocker, Handbook of Mathematics and Computational Science, Springer-Verlag New York, 1998.
| |
[b5] via grobid |
W. Mao and J. Gratch, A Utility-Based Approach to Intention Recognition, Proceedings of the AAMAS 2004 Workshop on Agent Tracking: Modeling Other Agents from Observations (2004).
| |
[b6] via grobid |
L. Itti, C. Koch and E. Niebur, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, IEEE Trans. Pattern Analysis and Machine Intelligence. Vol.20(1998), No.11, pp.1254-1259.
| |
[b7] via grobid |
L. Itti and C. Koch, A saliency-based search mechanism for overt and covert shifts of visual attention, Vision Research. Vol.40(2000), pp.1489-1506.
| |
[b8] via grobid |
D. Walther, U. Rutishauser, C. Koch and P. Perona, On the usefulness of attention for object recognition, Workshop on Attention and Perfromance in Computational Vision. (2004) pp.96-103.
| |
[b9] via grobid |
L. Itti, Models of bottom-up and top-down visual attention, PhD thesis, California Institute of Technology, 2000.
| |
[b10] via grobid |
L. M. Hurvich and D. Jameson, An opponent-process theory of color vision, Psychological Review. vol.63, pp.384-404, 1957.
| |
[b11] via grobid |
R. Manduchi, P. Perona and D. Shy, Efficient deformable filter banks, IEEE Transactions on Signal Processing. Vol.46(1998), No.4, pp.1168-1173.
| |
[b12] via grobid |
W. O. Lee, J. W. Lee, K. R. Park, E. C. Lee and M. Whang, Object recognition and selection method by gaze tracking and SURF algorithm, 2011 International Conference on Multimedia and Signal Processing. (2011) pp.261-265.
| |
[b13] via fuzzy |
Extreme learning machine: a new learning scheme of feedforward neural networks
Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) unpublished doi:10.1109/ijcnn.2004.1380068 |
web.archive.org [PDF]
|
[b14] via fuzzy |
Supervisory Recurrent Fuzzy Neural Network Control of Wing Rock for Slender Delta Wings
C.-M. Lin, C.-F. Hsu 2004 IEEE transactions on fuzzy systems doi:10.1109/tfuzz.2004.834803 |
web.archive.org [PDF]
|
[b15] via fuzzy |
A Biologically Inspired Algorithm for the Recovery of Shading and Reflectance Images
Adriana Olmos, Frederick A A Kingdom 2004 Perception doi:10.1068/p5321 pmid:15729913 |
web.archive.org [PDF]
|