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 language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci. |
Publisher | Springer Nature |
Pages | 41-55 |
Number of pages | 14 |
Volume | 4109 |
ISBN (Print) | 3540372369, 9783540372363 |
DOIs | |
Publication status | Published - 2006 |
Event | Joint 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
Name | Lecture Notes in Computer Science |
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Conference
Conference | Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006 |
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City | Hong Kong |
Period | 1/07/06 → … |
Internet address |