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2. Young D. Kwon and Jin S. Lee, “A Stochastic Map Building Method for Mobile Robot using 2-D Laser Range Finder”, Autonomous Robots 7,187-200,1999

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BIBLIOGRAFIA

1. Lia Garcìa-Perez, Marìa c. Garcìa-Alegre, Angela Ribeiro, Domingo Guinea, and Jose Marìa Canas, “Perception and Tracking of Dynamic Objects for Optimization of Avoidance Strategies in Autonomous Piloting of Vehicles”, Spatial Cognition IV, LNAI 3343, pp. 500-517, 2005.

2. Young D. Kwon and Jin S. Lee, “A Stochastic Map Building Method for Mobile Robot using 2-D Laser Range Finder”, Autonomous Robots 7,187-200,1999

3. Joshua Bobruk, David Austin, “Laser Motion Detection and Hypothesis from a Mobile Platform”,2002

4. B.D.Lucas,T.Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision”, DARPA Image Understanding Workshop, April 1981,pp.121-130

5. James Bruce, Tucker Balch, Manuela Veloso, “Fast and Inexpensive Color Image Segmentation for Interactive Robots”

6. David Prewer, Les Kitchen, “Soft Image Segmentation by Weighted Linked Pyramid”

7. Darius Burschka, Stephen Lee and Gregory Hager, “Stereo-based Obstacle Avoidance in Indoor Environments with Active Sensor Re-Calibration”, Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington DC, May 2002

8. Stephen Se, David G. Lowe, James J. Little, “Vision-Based Global Localization and Mapping for Mobile Robots”, IEEE Transactions On Robotics, VOL.21,NO. 3, Giugno 2005

9. Marius Leordeanu, Robert Collins, “Unsupervised Learning of Object Features from Video Sequences”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.

10. A.K.Jain, M.N. Murty, P.J. Flynn, “Data Clustering: A Review”, ACM Computing Surveys, Vol. 31, No. 3,Settembre 1999

11. Mark Pauly, Markus Gross, Leif P.Kobbelt, “Efficient Simplification of Point-Based Surfaces”,2002

12. Nicolàs Lomenie, “A generic methodology for partitioning unorganized 3D point clouds for robotic vision”, First Canadian Conference on Computer and Robot Vision,2004.

13. W.Burger, J.Scharinger, ”Object-centered Feature Selection for Weakly-Unsupervised Object Categorization”, Digital Imaging in Media and Education ,Vol.179 of Schriftenreihe, pages 79-86. OCG,2004.

14. D.Lowe, “Distintive Image Features from Scale-Invariant Keypoint”, International Journal for Computer Vision,2004

15. J. H Jean, T.P. Wu, “Robust Visual Servo Control of a Mobile Robot for Object Tracking in Shape Parameter Space”, 43

rd

IEEE Conference On Decision and Control, 14-17 December 2004, Atlantis, Paradise Island, Bahamas

16. N.P. Papanikolopoulos, P.K. Khosla, T.Kanade, “Visual Tracking of Moving Target by a Camera Mounted on a Robot: A combination of Control and Vision”, IEEE Transaction on Robotics and Automation, Vol.9,No.1,pp.14-35,Feb.1993

17. W. Huang, E.P. Krotkov, “Optimal Stereo Mast Configuration for Mobile Robot”, International Conference on Robotics and Automation, Vol.3,Aprile 1997,pp. 1946-1951 18. Hartley, Ziesserman, “Multiple View Geometry in Computer Vision”, Cambrige

University Press, 2004

19. K.Mikolajczyk e C.Schmid, “A performance evaluation of local descriptors”, International Conference in Computer Vision, Giugno 2003.

20. Koenderink J.J., “The structure of images”, Biological Cybernetics, 50: 363,396, 1984 21. Lindeberg T. “Scale-space theory: A basic tool for analysing structures at different

scales”, Journal of Applied Statistics,1994, 21(2):224,270.

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113 22. Lowe D.G. , “Object recognition from local scale-invariant features”, International

Conference on Computer Vision, Corfu, Greece,1999,pp. 1150-1157

23. Brown M.,Lowe D.G., “Invariant Features from interest point groups”, In British Machine Vision Comference, Cardiff,Wales,2002, pp. 656-665.

24. Stephen Se, David Lowe, Jim Little, “Mobile Robot Localization and Mapping with Uncertanty using Scale-Invariant Visual Landmarks” , The international Journal of Robotics Research Vol.21,No.8, Agosto 2002, pp753-758.

25. A.Boggio, G.Borello, “STATISTICA Argomenti e applicazioni di inferenza statistica

e di interpolazione e regressione” ,PETRINI EDITORE,2000.

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