Intelligent instrument reader using computer vision and machine learning

Robert A. Sowah; Abdul R. Ofoli; Eugene Mensah-Ananoo; Godfrey A. Mills; Koudjo M. Koumadi

doi:10.1109/IAS.2018.8544601

Intelligent instrument reader using computer vision and machine learning

Robert A. Sowah, Abdul R. Ofoli, Eugene Mensah-Ananoo, Godfrey A. Mills, Koudjo M. Koumadi

Department of Computer Engineering

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

3 Citations (Scopus)

Abstract

A novel algorithm using computer vision and machine learning techniques have been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter including the graduation values and angles are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analogue meter automatically. The proposed approach was tested to read a variety of offline and live-feed images of analog pointer meters automatically without any prior information about the meters.

Original language	English
Title of host publication	2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538645369
DOIs	https://doi.org/10.1109/IAS.2018.8544601
Publication status	Published - 26 Nov 2018
Event	2018 IEEE Industry Applications Society Annual Meeting, IAS 2018 - Portland Duration: 23 Sep 2018 → 27 Sep 2018

Publication series

Name	2018 IEEE Industry Applications Society Annual Meeting, IAS 2018

Conference

Conference	2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
Country/Territory	United States
City	Portland
Period	23/09/18 → 27/09/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IAS.2018.8544601

Cite this

Sowah, R. A., Ofoli, A. R., Mensah-Ananoo, E., Mills, G. A., & Koumadi, K. M. (2018). Intelligent instrument reader using computer vision and machine learning. In 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018 Article 8544601 (2018 IEEE Industry Applications Society Annual Meeting, IAS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAS.2018.8544601

@inproceedings{d50b77678f9d4bc6a9c84e6a5efed239,

title = "Intelligent instrument reader using computer vision and machine learning",

abstract = "A novel algorithm using computer vision and machine learning techniques have been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter including the graduation values and angles are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analogue meter automatically. The proposed approach was tested to read a variety of offline and live-feed images of analog pointer meters automatically without any prior information about the meters.",

author = "Sowah, {Robert A.} and Ofoli, {Abdul R.} and Eugene Mensah-Ananoo and Mills, {Godfrey A.} and Koumadi, {Koudjo M.}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018 ; Conference date: 23-09-2018 Through 27-09-2018",

year = "2018",

month = nov,

day = "26",

doi = "10.1109/IAS.2018.8544601",

language = "English",

series = "2018 IEEE Industry Applications Society Annual Meeting, IAS 2018",

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

booktitle = "2018 IEEE Industry Applications Society Annual Meeting, IAS 2018",

}

Sowah, RA, Ofoli, AR, Mensah-Ananoo, E, Mills, GA & Koumadi, KM 2018, Intelligent instrument reader using computer vision and machine learning. in 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018., 8544601, 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018, Portland, United States, 23/09/18. https://doi.org/10.1109/IAS.2018.8544601

Intelligent instrument reader using computer vision and machine learning. / Sowah, Robert A.; Ofoli, Abdul R.; Mensah-Ananoo, Eugene et al.
2018 IEEE Industry Applications Society Annual Meeting, IAS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8544601 (2018 IEEE Industry Applications Society Annual Meeting, IAS 2018).

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

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T1 - Intelligent instrument reader using computer vision and machine learning

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AU - Ofoli, Abdul R.

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PY - 2018/11/26

Y1 - 2018/11/26

N2 - A novel algorithm using computer vision and machine learning techniques have been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter including the graduation values and angles are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analogue meter automatically. The proposed approach was tested to read a variety of offline and live-feed images of analog pointer meters automatically without any prior information about the meters.

AB - A novel algorithm using computer vision and machine learning techniques have been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter including the graduation values and angles are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analogue meter automatically. The proposed approach was tested to read a variety of offline and live-feed images of analog pointer meters automatically without any prior information about the meters.

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M3 - Conference contribution

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T3 - 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018

BT - 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018

Y2 - 23 September 2018 through 27 September 2018

ER -

Intelligent instrument reader using computer vision and machine learning

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UN SDGs

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