Structural inference of sensor-based measurements

Elzbieta Pekalska, Robert P W Duin, Elzbieta P̧ekalska

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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. © Springer-Verlag Berlin Heidelberg 2006.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages41-55
    Number of pages14
    Volume4109
    ISBN (Print)3540372369, 9783540372363
    DOIs
    Publication statusPublished - 2006
    EventJoint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006 - Hong Kong
    Duration: 1 Jul 2006 → …
    http://dblp.uni-trier.de/db/conf/sspr/sspr2006.html#DuinP06http://dblp.uni-trier.de/rec/bibtex/conf/sspr/DuinP06.xmlhttp://dblp.uni-trier.de/rec/bibtex/conf/sspr/DuinP06

    Publication series

    NameLecture Notes in Computer Science

    Conference

    ConferenceJoint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006
    CityHong Kong
    Period1/07/06 → …
    Internet address

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