Understanding the spatial organization of image regions by means of force histograms: a guided tour
Applying soft computing in defining spatial relations
Applying soft computing in defining spatial relations
The Use of Force Histograms for Affine-Invariant Relative Position Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Qualitative spatial referencing for natural human-robot interfaces
interactions - Robots!
3-D modeling of spatial referencing language for human-robot interaction
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Scene matching using F-histogram-based features with possibilistic C-means optimization
Fuzzy Sets and Systems
Speaking with spatial relations
International Journal of Intelligent Systems Technologies and Applications
Real-time moving object segmentation in H.264 compressed domain based on approximate reasoning
International Journal of Approximate Reasoning
Mapping natural language to imagery: placing objects intelligently
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Dialog-based 3D-image recognition using a domain ontology
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
A linguistic ontology of space for natural language processing
Artificial Intelligence
Topological and directional fuzzy query for spatial objects with indeterminate boundaries
ICAI'05/MCBC'05/AMTA'05/MCBE'05 Proceedings of the 6th WSEAS international conference on Automation & information, and 6th WSEAS international conference on mathematics and computers in biology and chemistry, and 6th WSEAS international conference on acoustics and music: theory and applications, and 6th WSEAS international conference on Mathematics and computers in business and economics
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Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, pattern recognition, and computer vision. High-level computer vision applications hold a great potential for fuzzy set theory because of its links to natural language. Linguistic scene description, a language-based interpretation of regions and their relationships, is one such application that is starting to bear the fruits of fuzzy set theoretic involvement. In this paper, we are expanding on two earlier endeavors. We introduce new families of fuzzy directional relations that rely on the computation of histograms of forces. These families preserve important relative position properties. They provide inputs to a fuzzy rule base that produces logical linguistic descriptions along with assessments as to the validity of the descriptions. Each linguistic output uses hedges from a dictionary of about 30 adverbs and other terms that can be tailored to individual users. Excellent results from several synthetic and real image examples show the applicability of this approach