Linguistic Description of Relative Positions in Images

Pascal Matsakis; James M. Keller; Laurent Wendling; Jonathon Marjamaa; Ozy Sjahputera

inria-00100498, version 1

Linguistic Description of Relative Positions in Images

Pascal Matsakis ()^a, James M. Keller^a, Laurent Wendling^b^, 1, Jonathon Marjamaa^a, Ozy Sjahputera^a

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 31, 4 (2001) 573-588

Abstract: Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, patternr 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, in one such application that is starting to bear the fruits of fuzzy set theoric involvment. In this paper, we are expanding on two earlier endeavors. We introduce new families 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.

a – UNIVERSITY OF MISSOURI
b – UNIVERSITE HENRI POINCARE
1: ISA (INRIA Lorraine - LORIA)
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)

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Submitted on: Tuesday, 26 September 2006 14:46:15
Updated on: Thursday, 28 September 2006 15:22:47

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%% inria-00100498, version 1
%% http://hal.inria.fr/inria-00100498
@article{matsakis:inria-00100498,
    hal_id = {inria-00100498},
    url = {http://hal.inria.fr/inria-00100498},
    title = {{Linguistic Description of Relative Positions in Images}},
    author = {Matsakis, Pascal and Keller, James M. and Wendling, Laurent and Marjamaa, Jonathon and Sjahputera, Ozy},
    abstract = {{Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, patternr 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, in one such application that is starting to bear the fruits of fuzzy set theoric involvment. In this paper, we are expanding on two earlier endeavors. We introduce new families 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.}},
    keywords = {force histograms; fuzzy logic; linguistic desciption; relative positions; spatial relations || histogramme de forcs; logique floue; description linguistique; positions relatives; relations spatiales},
    language = {English},
    affiliation = {ISA - INRIA Lorraine - LORIA},
    publisher = {none},
    pages = {573-588},
    journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics},
    volume = {31},
    number = {4 },
    note = {Article dans revue scientifique avec comit{\'e} de lecture. A01-R-156 || matsakis01a A01-R-156 || matsakis01a },
    audience = {not specified },
    year = {2001},
}