Aspects of optimal viewpoint selection and viewpoint fusion

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3852 LNCS, Issue , 2006, Pages 902-912

  Deinzer, Frank ^a   Denzler, Joachim ^b   Derichs, Christian ^a   Niemann, Heinrich ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DENSITY (OPTICAL); LEARNING SYSTEMS; OPTIMAL SYSTEMS; OPTIMIZATION; PROBLEM SOLVING; USER INTERFACES;

DENSITY TREES; OPTIMAL VIEWPOINT SELECTION; PARZEN ESTIMATION; VIEWPOINT FUSION;

OBJECT RECOGNITION;

EID: 33744900330     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11612704_90     Document Type: Conference Paper

References (18)

1. P. Lehel and E.E. Hemayed and A.A. Farag: Sensor Planning for a Trinocular Active Vision System. In: CVPR. (1999) II:306-312
2. Madsen, C., Christensen, H.: A Viewpoint Planning Strategy for Determining True Angles on Polyhedral Objects by Camera Alignment. PAMI 19 (1997)
3. Roy, S.D., Chaudhury, S., Banerjee, S.: Recognizing Large 3-D Objects through Next View Planning using an Uncalibrated Camera. In: ICCV 2001, Vancouver, Canada (2001) II: 276-281
4. Schiele, B., Crowley, J.: Transinformation for Active Object Recognition. In: ICCV 98, Bombay, India (1998) 249-254
5. Krebs, B., Burkhardt, M., Korn, B.: Handling Uncertainty in 3D Object Recognition using Bayesian Networks. In: ECCV 98, Berlin (1998) 782-795
6. Denzler, J., Brown, C.: Information theoretic sensor data selection for active object recognition and state estimation. PAMI 24 (2002)
7. Zhou, X., Comaniciu, D., Krishnan, A.: Conditional feature sensitivity: A unifying view on active recognition and feature selection. In: ICCV 03, Nice, France (2003)
8. Callari, G., P.Ferrie, F.: Active Object Recognition: Looking for Differences. International Journal of Computer Vision 43 (2001) 189-204
9. Isard, M., Andrew, B.: CONDENSATION - Conditional Density Propagation for Visual Tracking. IJCV 98 29 (1998) 5-28
10. Gräßl, C., Deinzer, F., Mattern, F., Niemann, H.: Improving Statistical Object Recognition Approaches by a Parameterization of Normal Distributions. Pattern Recognition and Image Analysis 14 (2004) 222-230
11. Parzen, E.: On the estimation of a probability density function and mode. Annals of Mathematical Statistics 33 (1962) 1065-1076
12. Viola, P.: Alignment by Maximization of Mutual Information. PhD thesis, MIT, Department of Electrical Engineering and Computer Science, Cambridge, Massachusetts (1995)
13. Thrun, S., Langford, J.: Monte carlo hidden Markov models. Technical Report CMU-CS-98-179, Carnegie Mellon University, Computer Science Department, Pittsburgh, PA (1998)
14. Sutton, R., Barto, A.: Reinforcement Learning. A Bradford Book, Cambridge, London (1998)
15. Bertsekas, D., Tstsiklis, J.: Neuro-Dynamic Programming. Athena Scientific (1996)
16. Deinzer, F., Denzler, J., Niemann, H.: Viewpoint Selection - Planning Optimal Sequences of Views for Object Recognition. In: Computer Analysis of Images and Patterns. Volume 2756 of LNCS., Springer (2003) 65-73
17. Törn, A., Žilinskas, A.: Global Optimization. Volume 350 of Lecture Notes in Computer Science. Springer, Heidelberg (1987)
18. Deinzer, F., Denzler, J., Niemann, H.: On Fusion of Multiple Views for Active Object Recognition. In: DAGM 2001, Berlin, Springer (2001) 239-245