Recurrent instance segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9910 LNCS, Issue , 2016, Pages 312-329

  Romera Paredes, Bernardino ^a   Torr, Philip Hilaire Sean ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Deep learning; Instance segmentation; Recurrent neural nets

Indexed keywords

DEEP LEARNING; IMAGE SEGMENTATION;

END TO END; JOINT LEARNING; LOSS FUNCTIONS; OCCLUSION HANDLING; PLANT PHENOTYPING; SPATIAL MEMORY; STATE-OF-THE-ART APPROACH;

RECURRENT NEURAL NETWORKS;

EID: 84990051024     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46466-4_19     Document Type: Conference Paper

References (49)

1. VA-ST: Smart specs. http://www.va-st.com/smart-specs
2. Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect partially overlapping instances. In: Computer Vision and Pattern Recognition (CVPR), pp. 3230–3237. IEEE (2013)
3. Trager, W., Jensen, J.B.: Human malaria parasites in continuous culture. Science 193(4254), 673–675 (1976)
4. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10584-0_20
5. Chen, Y.T., Liu, X., Yang, M.H.: Multi-instance object segmentation with occlusion handling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3470–3478 (2015)
6. Vineet, V., Warrell, J., Ladicky, L., Torr, P.H.: Human instance segmentation from video using detector-based conditional random fields. In: British Machine Vision Conference (BMVC), pp. 1–11 (2011)
7. Dehaene, S., Cohen, L.: Dissociable mechanisms of subitizing and counting: neuropsychological evidence from simultanagnosic patients. J. Exp. Psychol. Hum. Percept. Perform. 20(5), 958 (1994)
8. Porter, G., Troscianko, T., Gilchrist, I.D.: Effort during visual search and counting: insights from pupillometry. Q. J. Exp. Psychol. 60(2), 211–229 (2007)
9. Ladicky, L., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: What, where and how many? Combining object detectors and CRFs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 424–437. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_31
10. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition (CVPR), pp. 580–587. IEEE (2014)
11. Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 328–335. IEEE (2014)
12. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)
13. Liang-Chieh, C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: International Conference on Learning Representations (ICLR) (2015)
14. Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: IEEE International Conference on Computer Vision (ICCV) (2015)
15. Liang, X., Wei, Y., Shen, X., Yang, J., Lin, L., Yan, S.: Proposal-free network for instance-level object segmentation. arXiv preprint arXiv:1509.02636 (2015)
16. Tighe, J., Niethammer, M., Lazebnik, S.: Scene parsing with object instances and occlusion ordering. In: Computer Vision and Pattern Recognition (CVPR), pp. 3748–3755. IEEE (2014)
17. Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.C.: Layered object models for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1731–1743 (2012)
18. Zhang, Z., Schwing, A.G., Fidler, S., Urtasun, R.: Monocular object instance segmentation and depth ordering with CNNs. In: IEEE International Conference on Computer Vision (ICCV), pp. 2614–2622 (2015)
19. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
20. Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 545–552 (2009)
21. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015)
22. Gregor, K., Danihelka, I., Graves, A., Wierstra, D.: Draw: a recurrent neu ral networkfor image generation. In: Proceedings of the 32nd International Conference on Machine Learning (ICML) (2015)
23. Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2625–2634 (2015)
24. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3128–3137 (2015)
25. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164 (2015)
26. Sønderby, S.K., Sønderby, C.K., Nielsen, H., Winther, O.: Convolutional LSTM networks for subcellular localization of proteins. In: Dediu, A.-H., Hernández-Quiroz, F., Martın-Vide, C., Rosenblueth, D.A. (eds.) AlCoB 2015. LNCS, vol. 9199, pp. 68–80. Springer, Heidelberg (2015). doi:10.1007/978-3-319-21233-3_6
27. Stewart, R., Andriluka, M.: End-to-end people detection in crowded scenes. arXiv preprint arXiv:1506.04878 (2015)
28. Pavel, M.S., Schulz, H., Behnke, S.: Recurrent convolutional neural networks for object-class segmentation of rgb-d video. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015)
29. Williams, C.K., Titsias, M.K.: Greedy learning of multiple objects in images using robust statistics and factorial learning. Neural Comput. 16(5), 1039–1062 (2004)
30. Ba, J., Mnih, V., Kavukcuoglu, K.: Multiple object recognition with visual attention. In: International Conference on Learning Representations (ICLR) (2015)
31. Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), pp. 2048–2057 (2015)
32. Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems (NIPS), pp. 2204–2212 (2014)
33. Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., Blunsom, P.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems (NIPS), pp. 1693–1701 (2015)
34. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
35. Dosovitskiy, A., Fischery, P., Ilg, E., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T., et al.: Flownet: learning optical flow with convolutional networks. In: IEEE International Conference on Computer Vision (ICCV), pp. 2758–2766. IEEE (2015)
36. Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems (NIPS), pp. 802–810 (2015)
37. Krähenbühl, P., Koltun, V.: Parameter learning and convergent inference for dense random fields. In: Proceedings of the 30th International Conference on Machine Learning (ICML), pp. 513–521 (2013)
38. Silberman, N., Sontag, D., Fergus, R.: Instance segmentation of indoor scenes using a coverage loss. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 616–631. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_40
39. Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS Workshop. Number EPFL-CONF-192376 (2011)
40. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
41. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10602-1_48
42. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 91–99 (2015)
43. Minervini, M., Abdelsamea, M.M., Tsaftaris, S.A.: Image-based plant phenotyping with incremental learning and active contours. Ecol. Inform. 23, 35–48 (2014)
44. Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recognition Letters Special Issue on Fine-grained Categorization in Ecological Multimedia (2015)
45. Pape, J.-M., Klukas, C.: 3-D histogram-based segmentation and leaf detection for rosette plants. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8928, pp. 61–74. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16220-1_5
46. Scharr, H., Minervini, M., French, A.P., Klukas, C., Kramer, D.M., Liu, X., Luengo Muntion, I., Pape, J.M., Polder, G., Vukadinovic, D., Yin, X., Tsaftaris, S.A.: Leaf segmentation in plant phenotyping: a collation study. Mach. Vision Appl. 27, 585–606 (2016)
47. Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: two new techniquesfor image matching. Technical report, DTIC Document (1977)
48. Yin, X., Liu, X., Chen, J., Kramer, D.M.: Multi-leaf tracking from fluorescence plant videos. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 408–412. IEEE (2014)
49. Giuffrida, M.V., Minervini, M., Tsaftaris, S.A.: Learning to count leaves in rosette plants. In: British Machine Vision Conference (CVPPP Workshop). BMVA Press (2015)