Segmentation techniques in image analysis: A comparative study
Nowadays, the detection, localization, and quantification of different kinds of features in an RGB image (segmentation) is extremely helpful for, e.g., process monitoring or customer product acceptance. In this article, some of the most commonly used RGB image segmentation approaches are compared in...
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Veröffentlicht in: | Journal of chemometrics 2016-12, Vol.30 (12), p.749-758 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | Nowadays, the detection, localization, and quantification of different kinds of features in an RGB image (segmentation) is extremely helpful for, e.g., process monitoring or customer product acceptance. In this article, some of the most commonly used RGB image segmentation approaches are compared in an orange quality control case study. Analysis of variance and correspondence analysis are combined for determining their most relevant differences and highlighting their pros and cons.
Nowadays, the detection, localization, and quantification of different kinds of features in an RGB image (segmentation) is extremely helpful for, e.g., process monitoring or customer product acceptance. In this article, some of the most commonly used RGB image segmentation approaches are compared in an orange quality control case study. Analysis of variance and correspondence analysis are combined for determining their most relevant differences and highlighting their pros and cons. |
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ISSN: | 0886-9383 1099-128X |
DOI: | 10.1002/cem.2854 |