Structural Stereopsis for 3-D Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Computational strategies for object recognition
ACM Computing Surveys (CSUR)
Hierarchical constraint logic programming
Hierarchical constraint logic programming
Computer and Robot Vision
Relational Matching
A Framework for Low Level Feature Extraction
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
A Brief Overview of Over-Constrained Systems
Over-Constrained Systems
Partial Constraint Satisfaction
Over-Constrained Systems
Partial constraint satisfaction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
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.