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Proceedings - OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
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[5] Michelle R. Greene, Christopher Baldassano, Andre Esteva, Diane M. Beck, and Li Fei-Fei. Visual scenes are categorized by function. Journal of Experimental Psychology: General, 145(1):82, 2016. [6] Bharath Hariharan, Pablo Arbela´ez, Ross Girshick, and Jitendra Malik. Hypercolumns for ob- ject segmentation and fine-grained localization. In Proceedings of the IEEE Conference on Computer VisionandPatternRecognition, pages447–456,2015. [7] PeterKontschieder,S.RotaBulo`,HorstBischof,andMarcelloPelillo. Structuredclass-labels in random forests for semantic image labelling. In Proceedings of IEEE International Conference onComputerVision, pages2190–2197,2011. [8] PhilippKra¨henbu¨hlandVladlenKoltun. Efficient inference in fullyconnectedCRFswithgaus- sianedge potentials. In Advances inNeural Information ProcessingSystems, 2011. [9] Ming-Yu Liu, Shuoxin Lin, Srikumar Ramalingam, and Oncel Tuzel. Layered interpretation of streetviewimages. InProceedings ofRobotics: ScienceandSystems,Rome, Italy, July 2015. [10] George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41,1995. [11] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic seg- mentation in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. [12] AudeOlivaandAntonioTorralba. Theroleofcontext inobject recognition. Trends inCognitive Sciences, 11(12):520–527, 2007. [13] George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, and Alan L. Yuille. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In Proceedings of the IEEE International Conference on Computer Vision, pages 1742–1750, 2015. [14] TimoScharwa¨chter,MarkusEnzweiler,UweFranke,andStefanRoth. Stixmantics: Amedium- level model for real-time semantic scene understanding. In Proceedings of the European Con- ferenceonComputer Vision, pages 533–548.Springer, 2014. [15] Abhishek Sharma, Oncel Tuzel, and David W. Jacobs. Deep hierarchical parsing for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pages 530–538, 2015. [16] Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi. Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, andcontext. International JournalofComputerVision, 81(1):2–23,2009. [17] Antonio Torralba and Alexei A. Efros. Unbiased look at dataset bias. In Proceedings of the IEEEConferenceonComputer VisionandPatternRecognition, pages1521–1528,2011. [18] Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip H.S. Torr. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 1529–1537,2015. 34
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Proceedings OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Titel
Proceedings
Untertitel
OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Autoren
Peter M. Roth
Kurt Niel
Verlag
Verlag der Technischen Universität Graz
Ort
Wels
Datum
2017
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-85125-527-0
Abmessungen
21.0 x 29.7 cm
Seiten
248
Schlagwörter
Tagungsband
Kategorien
International
Tagungsbände

Inhaltsverzeichnis

  1. Learning / Recognition 24
  2. Signal & Image Processing / Filters 43
  3. Geometry / Sensor Fusion 45
  4. Tracking / Detection 85
  5. Vision for Robotics I 95
  6. Vision for Robotics II 127
  7. Poster OAGM & ARW 167
  8. Task Planning 191
  9. Robotic Arm 207
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