Learning spatial relationships between objects

International Journal of Robotics Research

Volumn 30, Issue 11, 2011, Pages 1328-1342

  Rosman, Benjamin ^a,b   Ramamoorthy, Subramanian ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b DSI CSIR Nanotechnology Innovation Centre   (South Africa)

Author keywords

inter object relationships; qualitative representation; Semantic scene understanding; topology based abstraction

Indexed keywords

COMMONSENSE REASONING; CONTACT POINTS; INTER-OBJECT RELATIONSHIPS; LAYERED REPRESENTATION; POINT CLOUD; QUALITATIVE REPRESENTATION; SPATIAL RELATIONSHIPS; STEREO-IMAGE; TOPOLOGICAL FEATURES; TOPOLOGICAL REPRESENTATION; TOPOLOGY-BASED ABSTRACTION;

DECISION MAKING; SEMANTICS; TOPOLOGY;

ALGORITHMS;

EID: 80053987154     PISSN: 02783649     EISSN: 17413176     Source Type: Journal    
DOI: 10.1177/0278364911408155     Document Type: Article

References (28)

1. Alexe B, Deselaers T, Ferrari V. What is an object?. International Conference on Computer Vision and Pattern Recognition ; 2010 ; 2010. 73.
2. Barck-Holst C, Ralph M, Holmar F, Kragic D. Learning grasping affordance using probabilistic and ontological approaches. International Conference on Advanced Robotics ; 2009 ; 2009..
3. Bishop CM Pattern Recognition and Machine Learning. New York: Springer ; 2006 :
4. Calabar P, Santos PE. Formalising the fisherman's folly puzzle. Artificial Intelligence. 2011 ; 175: 346-377
5. Carlsson G. Topology and data. Bulletin of the American Mathematical Society. 2009 ; 46: 255-308
6. Cook DJ, Holder LB Mining Graph Data. New York: John Wiley and Sons ; 2007 :
7. Dee HM, Hogg DC, Cohn AG COSIT-09. Berlin: Springer ; 2009: 295-311.
8. Desai C, Ramanan D, Fowlkes C. Discriminative models for multi-class object layout. International Conference on Computer Vision ; 2009 ; 2009. 229.
9. Galata A, Cohn AG, Magee DR, Hogg DC. Modeling interaction using learnt qualitative spatio-temporal relations and variable length Markov models. European Conference on Artificial Intelligence ; 2002 ; 2002. 741.
10. Galleguillos C, Belongie S. Context based object categorization: A critical survey. Computer Vision and Image Understanding. 2010 ; 114: 712-722
11. Galleguillos C, Rabinovich A, Belongie S. Object categorization using co-occurence, location and appearance. International Conference on Computer Vision and Pattern Recognition ; 2008 ; 2008..
12. Ghallab M, Nau DS, Traverso P Automated Planning: Theory and Practice. San Mateo, CA: Morgan Kaufmann ; 2004 :
13. Gibson JJ The Ecological Approach to Visual Perception. 1986 ) The Ecological Approach to Visual Perception, 2nd edition.Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Mahwah, NJ: Lawrence Erlbaum Associates, Inc. ; 1986 :
14. Hauser K, Latombe J-C. Multi-modal motion planning in non-expansive spaces. The International Journal of Robotics Research. 2010 ; 29: 897-915
15. Jain AK, Dorai C. 3D object recognition: Representation and matching. Statistics and Computing. 2000 ; 10: 167-182
16. Jiang X, Bowyer K, Morioka Y, Hiura S, Sato K, Inokuchi S, et al. Some further results of experimental comparison of range image segmentation algorithms. International Conference on Pattern Recognition ; 2000 ; 2000. 877.
17. Joachims T Advances in Kernel Methods-Support Vector Learning. Cambridge, MA: The MIT Press ; 1999 :
18. Katz D, Brock O. Manipulating articulated objects with interactive perception. International Conference on Robotics and Automation ; 2008 ; 2008. 272.
19. Kuipers BJ, Beeson P, Modayil J, Provost J. Bootstrap learning of foundational representations. Connection Science. 2006 ; 18: 145-158 (Pubitemid 43982719)
20. Leibe B, Schiele B Scale-invariant Object Categorization using a Scale-adaptive Mean-shift Search. Berlin: Springer ; 2004: 145-153.
21. Maitin-Shepard J, Cusumano-Towner M, Lei J, Abbeel P. Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding. International Conference on Robotics and Automation ; 2010 ; 2010. 2308.
22. Pasula HM, Zettlemoyer LS, Kaelbling LP. Learning symbolic models of stochastic domains. Journal of Artificial Intelligence Research. 2007 ; 29: 309-352 (Pubitemid 47477595)
23. Pelleg D, Moore AW. X-means: Extending k-means with efficient estimation of the number of clusters. International Conference on Machine Learning ; 2000 ; 2000. 727.
24. Pinz A, Bischof H, Kropatsch W, Schweighofer G, Haxhimusa Y, Opelt A, et al. Representations for cognitive vision: A review of appearance-based, spatio-temporal, and graph-based approaches. Electronic Letters on Computer Vision and Image Analysis. 2008 ; 7: 35-61
25. Randell DA, Cui Z, Cohn AG. A spatial logic based on regions and connection. International Conference on Knowledge Representation and Reasoning ; 1992 ; 1992. 165.
26. Rusu RB, Holzbach A, Diankov R, Bradski G, Beetz M. Perception for mobile manipulation and grasping using active stereo. Humanoids. 2009 ;: 632-638
27. Saxena A, Driemeyer J, Ng AY. Robotic grasping of novel objects using vision. The International Journal of Robotics Research. 2008 ; 27: 157-173 (Pubitemid 351169712)
28. Waltz DL The Psychology of Computer Vision. New York: McGraw-Hill ; 1975: 19-92.