Treffer: Partial Similarity of Objects, or How to Compare a Centaur to a Horse
Title:
Partial Similarity of Objects, or How to Compare a Centaur to a Horse
Source:
Scale Space and Variational Methods in Computer VisionInternational journal of computer vision. 84(2):163-183
Publisher Information:
Heidelberg: Springer, 2009.
Publication Year:
2009
Physical Description:
print, 1 p.3/4
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse multicritère, Multicriteria analysis, Análisis multicriterio, Appariement chaîne, String matching, Chaîne caractère, Character string, Cadena carácter, Dimension Hausdorff, Hausdorff dimension, Dimensión Hausdorff, Distance, Distancia, Invariant, Invariante, Métrique, Metric, Métrico, Optimisation, Optimization, Optimización, Optimum Pareto, Pareto optimum, Optimo Pareto, Séquence image, Image sequence, Secuencia imagen, Texte, Text, Texto, Traitement image, Image processing, Procesamiento imagen, Vision ordinateur, Computer vision, Visión ordenador, Correspondence, Deformation-invariant similarity, Edit distance, Gromov-Hausdorff distance, Levenshtein distances, Metric geometry, Multicriterion optimization, Non-rigid shapes, Pareto optimality, Partial similarity, Shape similarity
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, Technion―Israel Institute of Technology, Haifa 32000, Israel
ISSN:
0920-5691
Rights:
Copyright 2009 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.21804591
Database:
PASCAL Archive
Weitere Informationen
Similarity is one of the most important abstract concepts in human perception of the world. In computer vision, numerous applications deal with comparing objects observed in a scene with some a priori known patterns. Often, it happens that while two objects are not similar, they have large similar parts, that is, they are partially similar. Here, we present a novel approach to quantify partial similarity using the notion of Pareto optimality. We exemplify our approach on the problems of recognizing non-rigid geometric objects, images, and analyzing text sequences.