|
1. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’14), 2014, Jun 23 - 28, Columbus, OH, USA. Piscataway, NJ, USA: IEEE, 2014: 580 - 587
2. Girshick R. Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision ( ICCV’15 ), 2015, Dec 7 - 13, Santiago, Chile. Piscataway, NJ, USA: IEEE, 2015: 1440 - 1448
3. Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28: Proceedings of the 29th Annual Conference on Neural Information Processing Systems: Vol 1, 2015, Dec 7 - 12, Montreal, Canada. Red Hook, NY,USA: Curran Associates Inc, 2015: 91 - 99
4. He K M, Gkioxari G, Doll佗r P, et al. Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision ( ICCV’17), 2017, Oct 22 - 29, Venice, Italy. Piscataway, NJ, USA: IEEE, 2017: 2980 - 2988
5. Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. Proceedings of the 14th European Conference on Computer Vision ( ECCV’16 ), 2016, Oct 8 - 16, Amsterdam, Netherlands. LNCS 9905. Berlin, Germany: Springer, 2016: 21 - 37
6. Redmon J, Divvala S, Girshick R, et al. You only look once: unifled, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’16 ), 2016, Jun 27 - 30, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2016: 779 - 788
7. Rossi M, Frossard P. Geometry-consistent light field super- resolution via graph-based regularization. IEEE Transactions on Image Processing, 2018, 27(9): 4207 - 4218
8. Gershun A. The light field. Journal of Mathematics and Physics, 1939, 18(1 / 2 / 3 / 4): 51 - 151
9. Adelson E H, Bergen J R. The plenoptic function and the elements of early vision. Computational Models of Visual Processing, 1991, 1(2): 3 - 20
10. Levoy M, Hanrahan P. Light field rendering. Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques ( SIGGRAPH’96 ), 1996, Aug 4 - 9, New Orleans, LA, USA. New York, NY, USA: ACM, 1996: 31 - 42
11. Adelson E H, Wang J Y A. Single lens stereo with a plenoptic camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 99 - 106
12. Ng R. Digital light field photography. Ph D Thesis. Palo Alto, CA, USA: Stanford University, 2006
13. Jeon H G, Park J, Choe G, et al. Accurate depth map estimation from a lenslet light field camera. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’15), 2015, Jun 7 - 12, Boston, MA, USA. Piscataway, NJ, USA: IEEE, 2015: 1547 - 1555
14. Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861 - 2873
15. Gao X B, Zhang K B, Tao D C, et al. Joint learning for single-image super-resolution via a coupled constraint. IEEE Transactions on Image Processing, 2012, 21(2): 469 - 480
16. Dong C, Loy C C, He K M, et al. Learning a deep convolutional network for image super-resolution. Proceedings of the 13th European Conference on Computer Vision ( ECCV’14 ): Part IV, 2014, Sept 6 - 12, Zurich, Switzerland. LNCS 8692. Berlin, Germany: Springer, 2014: 184 - 199
17. Bishop T E, Zanetti S, Favaro P. Light field superresolution. Proceedings of the 2009 IEEE International Conference on Computational Photography ( ICCP’09), 2009, Apr 16 - 17, San Francisco, CA, USA. Piscataway, NJ, USA: IEEE, 2009: 1 - 9
18. Bishop T E, Favaro P. Full-resolution depth map estimation from an aliased plenoptic light field. Proceedings of the 10th Asian Conference on Computer Vision ( ACCV’10 ): Part II, 2010, Nov 8 - 12, Queenstown, New Zealand. LNCS 6493. Berlin, Germany: Springer, 2010: 186 - 200
19. Wanner S, Goldluecke B. Spatial and angular variational super-resolution of 4D light fields. Proceedings of the 12th European Conference on Computer Vision ( ECCV’12 ): Part V, 2012, Oct 7 - 13, Florence, Italy. LNCS 7576. Berlin, Germany: Springer, 2012: 608 - 621
20. Wanner S, Goldluecke B. Variational light field analysis for disparity estimation and super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36 ( 3 ): 606 - 619
21. Mitra K, Veeraraghavan A. Light field denoising, light field superresolution and stereo camera based refocussing using a GMM light field patch prior. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW’12), 2012, Jun 16 - 21, Providence, RI, USA. Piscataway, NJ, USA: IEEE, 2012: 22 - 28
22. Cho D, Kim S, Tai Y W. Consistent matting for light field images. Proceedings of the 13th European Conference on Computer Vision
( ECCV’14): Part IV, 2014, Sept 6 - 12, Zurich, Switzerland. LNCS 8692. Berlin, Germany: Springer, 2014: 90 - 104
23. Cui Z, Chang H, Shan S G, et al. Deep network cascade for image super-resolution. Proceedings of the 13th European Conference on
Computer Vision ( ECCV’14 ): Part V, 2014, Sept 6 - 12, Zurich, Switzerland. LNCS 8693. Berlin, Germany: Springer, 2014: 49 - 64
24. Yoon Y, Jeon H G, Yoo D, et al. Learning a deep convolutional network for light-field image super-resolution. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop ( ICCVW’15 ), 2015, Dec 7 - 13, Santiago, Chile. Piscataway, NJ, USA: IEEE, 2015: 57 - 65
25. Yoon Y, Jeon H G, Yoo D, et al. Light-field image super-resolution using convolutional neural network. IEEE Signal Processing Letters, 2017, 24(6): 848 - 852
26. Gul M S K, Gunturk B K. Spatial and angular resolution enhancement of light fields using convolutional neural networks. IEEE Transactions on Image Processing, 2018, 27 ( 5 ): 2146 -2159
27. Wang Y L, Liu, F, Zhang K B, et al. LFNet: a novel bidirectional recurrent convolutional neural network for light-field image super-resolution. IEEE Transactionss on Image Processing, 2018, 27(9): 4274 - 4286
28. Zhang S, Lin Y F, Sheng H. Residual networks for light fieldimage super-resolution. Proceedings of the 2019 IEEE / CVF Conferenceon Computer Vision and Pattern Recognition ( CVPR’19 ), 2019, Jun 15 - 20, Long Beach, CA, USA. Piscataway, NJ, USA: IEEE, 2019: 11038 - 11047
29. Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks. Proceedings of the 2016 IEEE Conferenceon on Computer Vision and Pattern Recognition ( CVPR’16 ), 2016, Jun 27 - 30, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2016: 1646 - 1654
30. Lim B, Son S, Kim H, et al. Enhanced deep residual networks for single image super-resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW’17 ), 2017, Jul 21 - 26, Honolulu, HI, USA. Piscataway, NJ, USA: IEEE, 2017: 1132 - 1140
31. Zhang Y L, Tian Y P, Kong Y, et al. Residual dense network for image super-resolution. Proceedings of the 2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition, 2018, Jun 18 - 23, Salt Lake City, UT, USA. Piscataway, NJ, USA: IEEE, 2018: 2472 - 2481
32. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition ( CVPR’01), 2001, Dec 8 - 14, Kauai, HI, USA. Piscataway, NJ, USA: IEEE, 2001: I. 511 - I. 518
33. Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137 - 154
34. Dalal N, Triggs B. Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition ( CVPR’05): Vol 1, 2005, Jun 20 - 25, San Diego, CA, USA. Piscataway, NJ, USA: IEEE, 2005: 886 - 893
35. Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’08 ), 2008, Jun 23 - 28, Anchorage, AK, USA. Piscataway, NJ, USA: IEEE, 2008: 1 - 8
36. He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904 - 1916
37. Zhao X, Ni Y T, Jia H H. Modifled object detection method based on YOLO’. In: Yang J. et al. ( eds) Computer Vision. CCCV 2017. Communications in Computer and Information Science, 2017, 773: 233 - 244
38. Lin T Y, Gogal P, Girshick R, et al. Focal loss for dense object detection. Proceedings of the 2nd CCF Chinese Conference on Computer Vision ( CCCV’17 ): Part III, 2017, Oct 11 - 14, Tianjin, China. CCIS 773. Berlin, Germany: Springer, 2017: 2999 - 3007
39. Redmon J, Farhadi A. YOLOv3: an incremental improvement. arXiv Preprint, 2018, arXiv:180402767
40. Redmon J, Farhadi A. YOLO9000: better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’17 ), 2017, Jul 21 - 26, Honolulu, HI, USA. Piscataway, NJ, USA: IEEE, 2017: 7263 - 7266
|
