Result: Structural inference of sensor-based measurements

Title:
Structural inference of sensor-based measurements
Authors:
DUIN, Robert P. W, PEKALSKA, Elzbieta
Source:
Structural, syntactic, and statistical pattern recognition (joint IAPR international workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006)0SSPR 2006. :41-55
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 30 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse statistique, Statistical analysis, Análisis estadístico, Analyse structurale, Structural analysis, Análisis estructural, Analyse syntaxique, Syntactic analysis, Análisis sintáxico, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Capteur mesure, Measurement sensor, Captador medida, Chronométrie, Time measurement, Cronometría, Détecteur image, Image sensor, Detector imagen, Inférence, Inference, Inferencia, Modélisation, Modeling, Modelización, Reconnaissance forme, Pattern recognition, Reconocimiento patrón
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
ICT group, Faculty of Electr. Eng., Mathematics and Computer Science Delft University of Technology, Netherlands
School of Computer Science, University of Manchester, United Kingdom
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19151953
Rights:
Copyright 2007 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.19151953
Database:
PASCAL Archive

Further Information

Statistical inference of sensor-based measurements is intensively studied in pattern recognition. It is usually based on feature representations of the objects to be recognized. Such representations, however, neglect the object structure. Structural pattern recognition, on the contrary, focusses on encoding the object structure. As general procedures are still weakly developed, such object descriptions are often application dependent. This hampers the usage of a general learning approach. This paper aims to summarize the problems and possibilities of general structural inference approaches for the family of sensor-based measurements: images, spectra and time signals, assuming a continuity between measurement samples. In particular it will be discussed when probabilistic assumptions are needed, leading to a statistically-based inference of the structure, and when a pure, non-probabilistic structural inference scheme may be possible.