• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
H. Hirschmuller, 2007, Stereo processing by semiglobal matching and mutual information, IEEE Transactions on pattern analysis and machine intelligence, Vol. 30, No. 2, pp. 328-341DOI
2 
N. Bernini, et al., 2014, Real-time obstacle detection using stereo vision for autonomous ground vehicles: A survey., in Proc. of 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 873-878DOI
3 
S. Ramos, et al., 2017, Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling, 2in, pp. 1025-1032DOI
4 
B. Ruf, et al., 2018, Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision, arXiv preprint arXiv:1807.06271DOI
5 
P. Viola, W. M. Wells III, 1997, Alignment by maximization of mutual information, International Journal of Computer Vision, Vol. 24, No. 2, pp. 137-154DOI
6 
A. Seki, M. Pollefeys, 2017, Sgm-nets: Semi-global matching with neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 231-240Google Search
7 
J. Žbontar, Y. LeCun, 2015, Computing the stereo matching cost with a convolutional neural network, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1592-1599Google Search
8 
J. Žbontar, Y. LeCun, 2016, Stereo matching by training a convolutional neural network to compare image patches, The Journal of Machine Learning Research, Vol. 17, No. 1, pp. 2287-2318Google Search
9 
A. Kendall, et al., 2017, End-to-end learning of geometry and context for deep stereo regression, Proceedings of the IEEE International Conference on Computer Vision, pp. 66-75Google Search
10 
Z. Liang, et al., 2018, Learning for disparity estimation through feature constancy, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2811-2820Google Search
11 
S. Kim, et al., 2018, Unified confidence estimation networks for robust stereo matching, IEEE Transactions on Image Processing, Vol. 28, No. 3, pp. 1299-1313DOI
12 
Y. Luo, et al., 2018, Single view stereo matching, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 155-163Google Search
13 
A. G. Howard, et al., 2017, Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861Google Search
14 
D. Scharstein, R. Szeliski, 2002, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, Vol. 47, No. 1-3, pp. 7-42DOI
15 
S. Khamis, et al., 2018, Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction, Proceedings of the European Conference on Computer Vision (ECCV), pp. 573-590Google Search