Arc and path consistence revisited
Artificial Intelligence
Knowledge Structuring and Constraint Satisfaction: The Mapsee Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Arc-consistency and arc-consistency again
Artificial Intelligence
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Semantic graph and arc consistency in "true" three dimensional image labelling
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Inducing semantic segmentation from an example
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
An optimal set of image segmentation rules
Pattern Recognition Letters
Computer Vision and Image Understanding
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
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The labeling of the regions of a segmented image according to a semantic representation (ontology) is usually associated with the notion of understanding. The high combinatorial aspect of this problem can be reduced with local checking of constraints between the elements of the ontology. In the classical definition of Finite Domain Constraint Satisfaction Problem, it is assumed that the matching problem between regions and labels is bijective. Unfortunately, in image interpretation the matching problem is often non-univocal. Indeed, images are often over-segmented: one object is made up of several regions. This non-univocal matching between data and a conceptual graph was not possible until a decisive step was accomplished by the introduction of arc consistency with bilevel constraint (FDCSP"B"C). However, this extension is only adequate for a matching corresponding to surjective functions. In medical image analysis, the case of non-functional relations is often encountered, for example, when an unexpected object like a tumor appears. In this case, the data cannot be mapped to the conceptual graph, with a classical approach. In this paper we propose an extension of the FDCSP"B"C to solve the constraint satisfaction problem for non-functional relations.