Holistically-nested edge detection

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 1395-1403

  Xie, Saining ^a   Tu, Zhuowen ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER VISION; IMAGE CLASSIFICATION; NEURAL NETWORKS; SIGNAL DETECTION;

CONVOLUTIONAL NEURAL NETWORK; EDGE CLASSIFICATION; EDGE DETECTION ALGORITHMS; HIERARCHICAL REPRESENTATION; IMAGE PREDICTION; MULTI-SCALE FEATURES; OBJECT BOUNDARIES; ORDERS OF MAGNITUDE;

EDGE DETECTION;

EID: 84973859794     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.164     Document Type: Conference Paper

References (41)

1. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. PAMI, 33(5):898-916, 2011.
2. G. Bertasius, J. Shi, and L. Torresani. Deepedge: A multiscale bifurcated deep network for top-down contour detection. In CVPR, 2015.
3. P. Buyssens, A. Elmoataz, and O. Lézoray. Multiscale convolutional neural networks for vision-based classification of cells. In ACCV. 2013.
4. J. Canny. A computational approach to edge detection. PAMI, (6):679-698, 1986.
5. P. Dollar, Z. Tu, and S. Belongie. Supervised learning of edges and object boundaries. In CVPR, 2006.
6. P. Dollár and C. L. Zitnick. Fast edge detection using structured forests. PAMI, 2015.
7. J. H. Elder and R. M. Goldberg. Ecological statistics of gestalt laws for the perceptual organization of contours. Journal of Vision, 2(4):5, 2002.
8. C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning hierarchical features for scene labeling. PAMI, 2013.
9. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graphbased image segmentation. IJCV, 59(2):167-181, 2004.
10. Y. Ganin and V. Lempitsky. N4-fields: Neural network nearest neighbor fields for image transforms. ArXiv preprint arXiv:1406. 6558, 2014.
11. S. Gupta, P. Arbelaez, and J. Malik. Perceptual organization and recognition of indoor scenes from rgb-d images. In CVPR, 2013.
12. S. Gupta, R. Girshick, P. Arbeláez, and J. Malik. Learning rich features from rgb-d images for object detection and segmentation. In ECCV, 2014.
13. S. Hallman and C. C. Fowlkes. Oriented edge forests for boundary detection. ArXiv preprint arXiv:1412. 4181, 2014.
14. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In CVPR, 2015.
15. D. Hoiem, A. A. Efros, and M. Hebert. Putting objects in perspective. IJCV, 80(1):3-15, 2008.
16. D. Hoiem, A. N. Stein, A. A. Efros, and M. Hebert. Recovering occlusion boundaries from a single image. In ICCV, 2007.
17. X. Hou, A. Yuille, and C. Koch. Boundary detection benchmarking: Beyond f-measures. In CVPR, 2013.
18. D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology, 160(1):106-154, 1962.
19. J.-J. Hwang and T.-L. Liu. Pixel-wise deep learning for contour detection. In ICLR, 2015.
20. J. Kittler. On the accuracy of the sobel edge detector. Image and Vision Computing, 1(1):37-42, 1983.
21. J. J. Kivinen, C. K. Williams, N. Heess, and D. Technologies. Visual boundary prediction: A deep neural prediction network and quality dissection. In AISTATS, 2014.
22. S. Konishi, A. L. Yuille, J. M. Coughlan, and S. C. Zhu. Statistical edge detection: Learning and evaluating edge cues. PAMI, 25(1):57-74, 2003.
23. C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu. Deeplysupervised nets. In AISTATS, 2015.
24. J. J. Lim, C. L. Zitnick, and P. Dollár. Sketch tokens: A learned mid-level representation for contour and object detection. In CVPR, 2013.
25. C. Liu, J. Yuen, and A. Torralba. Nonparametric scene parsing via label transfer. PAMI, 33(12):2368-2382, 2011.
26. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
27. D. Marr and E. Hildreth. Theory of edge detection. Proceedings of the Royal Society of London. Series B. Biological Sciences, 207(1167):187-217, 1980.
28. D. R. Martin, C. C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI, 26(5):530-549, 2004.
29. N. Neverova, C. Wolf, G. W. Taylor, and F. Nebout. Multiscale deep learning for gesture detection and localization. In ECCV Workshops, 2014.
30. X. Ren. Multi-scale improves boundary detection in natural images. In ECCV. 2008.
31. X. Ren and L. Bo. Discriminatively trained sparse code gradients for contour detection. In NIPS, 2012.
32. D. L. Ruderman and W. Bialek. Statistics of natural images: Scaling in the woods. Physical review letters, 73(6):814, 1994.
33. P. Sermanet, S. Chintala, and Y. LeCun. Convolutional neural networks applied to house numbers digit classification. In ICPR, 2012.
34. W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang. Deepcontour: A deep convolutional feature learned by positivesharing loss for contour detection draft version. In CVPR, 2015.
35. N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV. 2012.
36. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
37. V. Torre and T. A. Poggio. On edge detection. PAMI, (2):147-163, 1986.
38. Z. Tu. Auto-context and its application to high-level vision tasks. In CVPR, 2008.
39. D. C. Van Essen and J. L. Gallant. Neural mechanisms of form and motion processing in the primate visual system. Neuron, 13(1):1-10, 1994.
40. A. P. Witkin. Scale-space filtering: A new approach to multiscale description. In ICASSP, 1984.
41. A. L. Yuille and T. A. Poggio. Scaling theorems for zero crossings. PAMI, (1):15-25, 1986.