Integration of multiple sensors using binary features in a bernoulli mixture model

IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

Volumn , Issue , 2006, Pages 104-109

  Ferreira, F ^a   Santos, V ^a   Dias, J ^b  

^a UNIVERSITY OF AVEIRO   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS;

BERNOULLI MIXTURE MODELS; BINARY FEATURES; MULTIPLE SENSORS;

FEATURE EXTRACTION;

EID: 40949089299     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MFI.2006.265672     Document Type: Conference Paper

References (36)

1. Gregory Dudek and Paul MacKenzie. Model based map construction for mobile robot localization'. In Proceedings of Vision Interface, pages 97-102, Toronto, Ontario, July 1993.
2. Viet Nguyen, Agostino Martinelli, Nicola Tomatis, and Roland Siegwart. A comparison of line extraction algorithms using 2d laser rangefinder for indoor mobile robotics. In International Conference on Intelligent Robots and Systems, 2005.
3. Daniel Sack and Wolfram Burgard. A comparison of methods for line extraction from range data. In IAV 2004, 2004.
4. Maria Isabel Ribeiro and J Goncalves. Natural landmark based localisation of mobile robots using laser range data. In IEEE EUROBOT 96, 1996.
5. Artur Arsenio and Maria Isabel Ribeiro. Active range sensing for mobile robot localization. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robotic Systems, Victoria, Canada, October 1998.
6. Dinesh Manandhar and Ryosuke Shibasaki. Feature extraction from range data. In ACRS 2001 - 22nd Asian Conference on Remote Sensing, volume 2, pages 1113-1118, Singapore, November 2001.
7. James Fellers Sooyong Lee, Nancy M. Amato. Localization based on Visibility Sectors using Range Sensors. In Procceedings of the IEEE Int. Conference Robot. Autom. (ICRA), pages 3505-3511, Jan 2000.
8. Simon Baker. Design and Evaluation of Feature Detectors. PhD thesis, Columbia University, 1998.
9. Jan J Koenderink. The Structure Of Images. Biological Cybernetics, 50(5):363-370, 1984.
10. A.P.Witkin. Scale-space filtering. In Proc. 8th Joint conference on Artificial Intelligence, pages 1019-1023, Karlsruhe, W. Germany, 1983. 8th Joint conference on Artificial Intelligence.
11. Tony Lindeberg. Scale-Space Theory in Computer Vision. Kluwer Academic Press, 1994.
12. David G. Lowe. Object Recognition from Local Scale-Invariant Features. In Proc. of the International Conference on Computer Vision, Corfu, pages 1150-1157, 1999.
13. Ming-Kuei Hu. Visual pattern recognition by moment invariants. IEEE Transactions on Information Theory, 8(2):179-187, February 1962.
14. Henrik Andreasson and Tom Duckett. Topological Localization For Mobile Robots Using Omni-Directional Vision And Local Features. In 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles. Instituto Superior Tenico, Lisboa, Portugal, July 2004. IFAC/EURON.
15. Jukka Iivarinen, Markus Peura, Jaakko Sänelä, and Ari Visay. Comparison of combined shape descriptors for irregular objects. In British Machine Vision Conference, 1997.
16. Carmen de Trazegnies Otero. 3D Object Learning and Recognition System Based on Planar Views. PhD thesis, Universidad de Málaga, Málaga, Spain, 2004. ISBN:84-689-5286-9.
17. K. Arras and N. Tomatis. Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision. In 3rd Eumpean Workshop on Advanced Mobile Robots, 1999.
18. Haris Baltzakis, Antonis Argyros, and Panos Trahanias. Fusion of range and visual data for the extraction of scene structure information. In Proceedings of the International Conference On Pattern Recognition, pages 40007-40012, Quebec, Canada, August 2002.
19. Sajama and Alon Orlitsky. Supervised dimensionality reduction using mixture models. In Proceedings of the 22 nd International Conference on Machine Learning, Bonn, Germany, 2005.
20. Geoffrey McLachlan and David Peel. Finite Mixture Models. John Wiley and Sons, 2000.
21. Arthur Pope and David G. Lowe. Probabilistic Models of Appearance for 3-D Object Recognition. IJCV, 40(2):149-167, 2000.
22. David G. Lowe. Local Feature View Clustering for 3D Object Recognition. In Proceeding of the IEEE Conference on Computer-Vision and Pattern Recognition, pages 682-688, Kauai, Hawaii, 2001.
23. Matthew Brown and David G. Lowe. Recognising Panoramas. In Tenth International Conference on Computer Vision (ICCV 2003), October 2003.
24. Yan Ke, Rahul Sukdiankar, and Larry Huston. An efficient parts-based near-duplicate and sub-image retrieval system. In MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia, pages 869-876, New York, NY, USA. 2004. ACM Press.
25. Stephen Se, David Lowe, and Jim Little. Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks. International Journal of Robotics Research, 21(8):735-760, August 2002.
26. David M. Bradley, Rashmi Patel, Nicolas Vandapel, and Scott M. Thayer. Real-Time Image-Based Topological Localization in Large Outdoor Environments. In Pmceedings of the 2005 IEEE Conf. on Intelligent Robotics and Systems, August 2005.
27. Nicolas Cuperlier, Mathias Quoy, and Philippe Gaussier. Navigation and Planning in an Unknown Environent Using Vision and a Cognitive Map. In In Proceedings of The 2006 European Robotics Symposium, 2006.
28. Cordelia Schmid, Roger Mohr, and Christian Bauckhage. Comparing and Evaluating Interest Points. In ICCV, pages 230-235, Bombay India. 1998. Narosa Publishing House.
29. David G. Lowe. Distinctive Image Features-From Scale-Invariant Keypoints. IJCV, 60, Issue 2(Issue 2):91-119, November 2004.
30. Matthew B Kennel. KDTREE 2: Fortran 95 and C++ software to efficiently search for near neighbors in a multi-dimensional Euclidean space, 2004.
31. Ata Kaban and Mark Girolami. Initialized and Guided EM-Clustering of Sparse Binary Data with Application to Text Based Documents. In Proceedings of the 15th International Conference on Pattern Recognition (ICPRW), volume 2, 2000.
32. Xin Wang and Ata Kaban. Finding Uninformative Features in Binary Data. In Sixth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL '05), 2005.
33. J. Gonzalez, A. Juan, P. Dupont, E. Vidal, and F. Casacuberta. A Bernoulli Mixture Model for Word Categorisation. In Symposium Nacional de Reconocimiento de Formas y Analises de Imagenes, 2001.
34. Alfons Juan and Enrique Vidal. Bernoulli Mixture Models for Binary Images. In International Conference on Pattern Recognition (ICPR'04), volume 3, pages 367-370, 2004.
35. José García-Hernández, Vicent Alabau, Alfons Juan, and Enrique Vidal. Bernoulli mixture-based classification. In A. R. Figueiras-Vidal et al., editor, Proc. of the LEARNING04, ISBN 84-688-8453-7, Elche (Spain), October 2004.
36. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B,34:1-38, 1977.