TY - GEN
T1 - Constraints for object recognition in aerial images - Handling of unobserved features
AU - Kolbe, Thomas H.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.
PY - 1998
Y1 - 1998
N2 - In this paper we will show how constraint solving methods can be applied for the recognition of buildings in aerial images. Object models are transformed to constraint representations which are matched against extracted image features. To cope with disturbances caused by occlusions and noise, we distinguish between the unobservability of a) relations between object parts and b) object parts themselves. Whereas other approaches for solving over-constrained problems suggest to reduce the relaxation of a variable to the relaxation of its incident constraints, we argue that both cases have to be treated separately. Information theory is applied to derive constraint weights on a probabilistic basis. We extend constraints and variables in a way which provides for an adequate integration of constraint violation and variable elimination on the one hand, and allows the determination of the maximum likelihood estimation for the matching between model and image on the other hand.
AB - In this paper we will show how constraint solving methods can be applied for the recognition of buildings in aerial images. Object models are transformed to constraint representations which are matched against extracted image features. To cope with disturbances caused by occlusions and noise, we distinguish between the unobservability of a) relations between object parts and b) object parts themselves. Whereas other approaches for solving over-constrained problems suggest to reduce the relaxation of a variable to the relaxation of its incident constraints, we argue that both cases have to be treated separately. Information theory is applied to derive constraint weights on a probabilistic basis. We extend constraints and variables in a way which provides for an adequate integration of constraint violation and variable elimination on the one hand, and allows the determination of the maximum likelihood estimation for the matching between model and image on the other hand.
UR - https://www.scopus.com/pages/publications/84892330692
U2 - 10.1007/3-540-49481-2_22
DO - 10.1007/3-540-49481-2_22
M3 - Conference contribution
AN - SCOPUS:84892330692
SN - 3540652248
SN - 9783540652243
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 309
BT - Principles and Practice of Constraint Programming – CP 1998 - 4th International Conference, CP 1998, Proceedings
A2 - Puget, Jean-Francois
A2 - Maher, Michael
PB - Springer Verlag
T2 - 4th International Conference on Principles and Practice of Constraint Programming, CP 1998
Y2 - 26 October 1998 through 30 October 1998
ER -