Enhanced morphological filtering for wavelet-based changepoint detection

Mattia Stasolla; Xavier Neyt

doi:10.1109/SITIS.2019.00021

Enhanced morphological filtering for wavelet-based changepoint detection

Mattia Stasolla, Xavier Neyt

Remote Sensing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a new method for the detection of abrupt changes (i.e. mean shifts) in time series. It is a follow-up to a previous article by the authors where, for the first time, the possibility of combining the multi-scale analysis capabilities of wavelets with mathematical morphology, a theoretical framework for the analysis of spatial structures, had been explored. The processing chain has been revised and enhanced in order to improve the overall results, and a performance assessment has been carried out to evaluate the accuracy and robustness of the method to noise, also providing a comparison with its original implementation.

Original language	English
Title of host publication	Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
Editors	Kokou Yetongnon, Albert Dipanda, Gabriella Sanniti di Baja, Luigi Gallo, Richard Chbeir
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	56-60
Number of pages	5
ISBN (Electronic)	9781728156866
DOIs	https://doi.org/10.1109/SITIS.2019.00021
Publication status	Published - Nov 2019
Event	15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019 - Sorrento, Italy Duration: 26 Nov 2019 → 29 Nov 2019

Publication series

Name	Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019

Conference

Conference	15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
Country/Territory	Italy
City	Sorrento
Period	26/11/19 → 29/11/19

Keywords

Changepoint detection
Mathematical morphology
Time series
Wavelets

Access to Document

10.1109/SITIS.2019.00021

Cite this

Stasolla, M., & Neyt, X. (2019). Enhanced morphological filtering for wavelet-based changepoint detection. In K. Yetongnon, A. Dipanda, G. Sanniti di Baja, L. Gallo, & R. Chbeir (Eds.), Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019 (pp. 56-60). Article 9067954 (Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SITIS.2019.00021

Stasolla, Mattia ; Neyt, Xavier. / Enhanced morphological filtering for wavelet-based changepoint detection. Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019. editor / Kokou Yetongnon ; Albert Dipanda ; Gabriella Sanniti di Baja ; Luigi Gallo ; Richard Chbeir. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 56-60 (Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019).

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title = "Enhanced morphological filtering for wavelet-based changepoint detection",

abstract = "This paper presents a new method for the detection of abrupt changes (i.e. mean shifts) in time series. It is a follow-up to a previous article by the authors where, for the first time, the possibility of combining the multi-scale analysis capabilities of wavelets with mathematical morphology, a theoretical framework for the analysis of spatial structures, had been explored. The processing chain has been revised and enhanced in order to improve the overall results, and a performance assessment has been carried out to evaluate the accuracy and robustness of the method to noise, also providing a comparison with its original implementation.",

keywords = "Changepoint detection, Mathematical morphology, Time series, Wavelets",

author = "Mattia Stasolla and Xavier Neyt",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019 ; Conference date: 26-11-2019 Through 29-11-2019",

year = "2019",

month = nov,

doi = "10.1109/SITIS.2019.00021",

language = "English",

series = "Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "56--60",

editor = "Kokou Yetongnon and Albert Dipanda and {Sanniti di Baja}, Gabriella and Luigi Gallo and Richard Chbeir",

booktitle = "Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019",

}

Stasolla, M & Neyt, X 2019, Enhanced morphological filtering for wavelet-based changepoint detection. in K Yetongnon, A Dipanda, G Sanniti di Baja, L Gallo & R Chbeir (eds), Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019., 9067954, Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 56-60, 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019, Sorrento, Italy, 26/11/19. https://doi.org/10.1109/SITIS.2019.00021

Enhanced morphological filtering for wavelet-based changepoint detection. / Stasolla, Mattia ; Neyt, Xavier.
Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019. ed. / Kokou Yetongnon; Albert Dipanda; Gabriella Sanniti di Baja; Luigi Gallo; Richard Chbeir. Institute of Electrical and Electronics Engineers Inc., 2019. p. 56-60 9067954 (Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper presents a new method for the detection of abrupt changes (i.e. mean shifts) in time series. It is a follow-up to a previous article by the authors where, for the first time, the possibility of combining the multi-scale analysis capabilities of wavelets with mathematical morphology, a theoretical framework for the analysis of spatial structures, had been explored. The processing chain has been revised and enhanced in order to improve the overall results, and a performance assessment has been carried out to evaluate the accuracy and robustness of the method to noise, also providing a comparison with its original implementation.

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Stasolla M , Neyt X. Enhanced morphological filtering for wavelet-based changepoint detection. In Yetongnon K, Dipanda A, Sanniti di Baja G, Gallo L, Chbeir R, editors, Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 56-60. 9067954. (Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019). doi: 10.1109/SITIS.2019.00021

Enhanced morphological filtering for wavelet-based changepoint detection

Abstract

Publication series

Conference

Keywords

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Cite this

Enhanced morphological filtering for wavelet-based changepoint detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

EORegions-Science: EORegions-Science

Cite this