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Conference Paper: TIME SERIES MODELING FOR TEXTURE ANALYSIS AND SYNTHESIS WITH APPLICATIONS TO CLOUD FIELD MORPHOLOGY STUDY.

TitleTIME SERIES MODELING FOR TEXTURE ANALYSIS AND SYNTHESIS WITH APPLICATIONS TO CLOUD FIELD MORPHOLOGY STUDY.
Authors
Jau, YingChiaChin, Roland TWeinman, James A
KeywordsINFORMATION THEORY - Data Compression
Issue Date1984
Citation
Proceedings - International Conference on Pattern Recognition, 1984, v. 2, p. 1219-1221 How to Cite?
AbstractThis paper presents a procedure to model texture fields using seasonal autoregressive, moving average models. The modeling of 2-D images has been formulated as a 1-D time series analysis problem. Properties such as directionality and clustering have been fully investigated and presented. The applications of this 1-D seasonal ARMA process to texture analysis, synthesis and data compression have been discussed. It was demonstrated that a cloud field image can be quantitatively defined and its surrogates can be synthesized by the model parameters. The implications for the quantitative study of cloud climatology is thus evident.
Persistent Identifierhttp://hdl.handle.net/10722/65576

 

DC FieldValueLanguage
dc.contributor.authorJau, YingChiaen_HK
dc.contributor.authorChin, Roland Ten_HK
dc.contributor.authorWeinman, James Aen_HK
dc.date.accessioned2010-08-31T07:16:14Z-
dc.date.available2010-08-31T07:16:14Z-
dc.date.issued1984en_HK
dc.identifier.citationProceedings - International Conference on Pattern Recognition, 1984, v. 2, p. 1219-1221en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65576-
dc.description.abstractThis paper presents a procedure to model texture fields using seasonal autoregressive, moving average models. The modeling of 2-D images has been formulated as a 1-D time series analysis problem. Properties such as directionality and clustering have been fully investigated and presented. The applications of this 1-D seasonal ARMA process to texture analysis, synthesis and data compression have been discussed. It was demonstrated that a cloud field image can be quantitatively defined and its surrogates can be synthesized by the model parameters. The implications for the quantitative study of cloud climatology is thus evident.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - International Conference on Pattern Recognitionen_HK
dc.subjectINFORMATION THEORY - Data Compressionen_HK
dc.titleTIME SERIES MODELING FOR TEXTURE ANALYSIS AND SYNTHESIS WITH APPLICATIONS TO CLOUD FIELD MORPHOLOGY STUDY.en_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChin, Roland T: rchin@hku.hken_HK
dc.identifier.authorityChin, Roland T=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.scopuseid_2-s2.0-0021632820en_HK
dc.identifier.volume2en_HK
dc.identifier.spage1219en_HK
dc.identifier.epage1221en_HK
dc.identifier.scopusauthoridJau, YingChia=6602782713en_HK
dc.identifier.scopusauthoridChin, Roland T=7102445426en_HK
dc.identifier.scopusauthoridWeinman, James A=7101645308en_HK

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