Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Recurrent Instance Segmentation

verfasst von : Bernardino Romera-Paredes, Philip Hilaire Sean Torr

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other, thus missing opportunities for joint learning. Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially. The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time. This net is provided with a spatial memory that keeps track of what pixels have been explained and allows occlusion handling. In order to train the model we designed a principled loss function that accurately represents the properties of the instance segmentation problem. In the experiments carried out, we found that our method outperforms recent approaches on multiple person segmentation, and all state of the art approaches on the Plant Phenotyping dataset for leaf counting.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Learning Semantic Deformation Flows with 3D Convolutional Networks
Nächstes Kapitel Individualness and Determinantal Point Processes for Pedestrian Detection
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
Literatur
1.
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)CrossRef
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)CrossRef
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)CrossRef
9.
Ladický, Ľ., 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 CrossRef
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)CrossRef
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.
22.
Gregor, K., Danihelka, I., Graves, A., Wierstra, D.: Draw: a recurrent neural network for 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., Martín-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 CrossRef
27.
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)CrossRefMATH
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)CrossRef
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.
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)CrossRef
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)CrossRef
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)
Metadaten
Titel
Recurrent Instance Segmentation
verfasst von
Bernardino Romera-Paredes
Philip Hilaire Sean Torr
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46465-7

Electronic ISBN: 978-3-319-46466-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46466-4_19

Premium Partner

    Bildnachweise