Model-based image interpretation under uncertainty and fuzziness

Autor: Isabelle Bloch
Přispěvatelé: Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Traitement du Signal et des Images (TSI), Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS), HAL, TelecomParis
Jazyk: angličtina
Rok vydání: 2013
Předmět:
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
spatial relations
Computer science
Fuzzy set
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
model-based segmentation and recognition
02 engineering and technology
ENCODE
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
0202 electrical engineering
electronic engineering
information engineering
Medical imaging
Segmentation
Constraint satisfaction problem
graphs
business.industry
020207 software engineering
Spatial intelligence
Pattern recognition
structural models
fuzzy modeling
Spatial relation
Image understanding
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]
Computer Science::Computer Vision and Pattern Recognition
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
020201 artificial intelligence & image processing
Artificial intelligence
constraint satisfaction problems
business
Semantic gap
Zdroj: Tenth International Workshop on Fuzzy Logic and Applications-WILF (invited conference)
Tenth International Workshop on Fuzzy Logic and Applications-WILF (invited conference), Nov 2013, Genoa, Italy. pp.171-183
Fuzzy Logic and Applications ISBN: 9783319031996
WILF
Popis: International audience; Structural models such as ontologies and graphs can encode generic knowledge about a scene observed in an image. Their use in spatial reasoning schemes allows driving segmentation and recognition of objects and structures in images. The developed methods include finding a best segmentation path in a graph, global solving of a constraint satisfaction problem, integrating prior knowledge in deformable models, and exploring images in a progressive fashion. Conversely, these models can be specified based on individual information resulting from the segmentation and recognition process. In particular models relying on spatial relations between structures are relevant and more flexible than shape models to be adapted to potential variations, multiple occurrences, or pathological cases. The problem of semantic gap is addressed by generating spatial representations (in the image space) of relations initially expressed in linguistic or symbolic form, within a fuzzy sets formalism. This allows coping with uncertainty and fuzziness, which are inherent both to generic knowledge and to image information.Applications in medical imaging and remote sensing imaging illustrate the proposed paradigm.
Databáze: OpenAIRE
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