A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying soft computing in defining spatial relations
Understanding the spatial organization of image regions by means of force histograms: a guided tour
Applying soft computing in defining spatial relations
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
R-Histogram: quantitative representation of spatial relations for similarity-based image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
R*-Histograms: efficient representation of spatial relations between objects of arbitrary topology
Proceedings of the 12th annual ACM international conference on Multimedia
Spatial Reasoning with Incomplete Information on Relative Positioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy spatial relationships for image processing and interpretation: a review
Image and Vision Computing
Proceedings of the 11th international conference on Theoretical foundations of computer vision
A graph-based fuzzy linguistic metadata schema for describing spatial relationships
Proceedings of the 2011 Visual Information Communication - International Symposium
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
Exploiting depth camera for 3D spatial relationship interpretation
Proceedings of the 4th ACM Multimedia Systems Conference
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Humans are quite adept at recognizing and labeling regions and objects in visual scenes. One of the cues for such labeling is the spatial relationships exhibited among the regions. This is usually coupled with the interpreter's understanding and expectations of scene content. For example, it is normally the case that, in a natural outdoor scene, the sky should be above the trees and that vehicles should be on a road. Context plays a very important role in the interpretation of an image. This determination of spatial relations has been a difficult task to automate. There have been several attempts at defining spatial relationships between regions in a digital image, most recently, with the use of fuzzy set theory. In this paper, we examine three methods for defining spatial relations to gain insight into this complex situation.