Structural inference of sensor-based measurements
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Abstract
Statisticalinferenceofsensor-basedmeasurementsisintensivelystudiedinpatternrecognition.Itisusuallybasedonfeaturerepresentationsoftheobjectstoberecognized.Suchrepresentations,however,neglecttheobjectstructure.Structuralpatternrecognition,onthecontrary,focussesonencodingtheobjectstructure.Asgeneralproceduresarestillweaklydeveloped,suchobjectdescriptionsareoftenapplicationdependent.Thishamperstheusageofagenerallearningapproach.
Thispaperaimstosummarizetheproblemsandpossibilitiesofgeneralstructuralinferenceapproachesforthefamilyofsensor-basedmeasurements:images,spectraandtimesignals,assumingacontinuitybetweenmeasurementsamples.Inparticularitwillbediscussedwhenprobabilisticassumptionsareneeded,leadingtoastatistically-basedinferenceofthestructure,andwhenapure,non-probabilisticstructuralinferenceschememaybepossible.
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