Human Body Shapes Anomaly Detection and Classification Using Persistent Homology
Accurate sizing systems of a population permit the minimization of the production costs of the textile apparel industry and allow firms to satisfy their customers. Hence, information about human body shapes needs to be extracted in order to examine, compare and classify human morphologies. In this p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/3/161 |
| _version_ | 1797613909221310464 |
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| author | Steve de Rose Philippe Meyer Frédéric Bertrand |
| author_facet | Steve de Rose Philippe Meyer Frédéric Bertrand |
| author_sort | Steve de Rose |
| collection | DOAJ |
| description | Accurate sizing systems of a population permit the minimization of the production costs of the textile apparel industry and allow firms to satisfy their customers. Hence, information about human body shapes needs to be extracted in order to examine, compare and classify human morphologies. In this paper, we use topological data analysis to study human body shapes. Persistence theory applied to anthropometric point clouds together with clustering algorithms show that relevant information about shapes is extracted by persistent homology. In particular, the homologies of human body points have interesting interpretations in terms of human anatomy. In the first place, anomalies of scans are detected using complete-linkage hierarchical clusterings. Then, a discrimination index shows which type of clustering separates gender accurately and if it is worth restricting to body trunks or not. Finally, Ward-linkage hierarchical clusterings with Davies–Bouldin, Dunn and Silhouette indices are used to define eight male morphotypes and seven female morphotypes, which are different in terms of weight classes and ratios between bust, waist and hip circumferences. The techniques used in this work permit us to classify human bodies and detect scan anomalies directly on the full human body point clouds rather than the usual methods involving the extraction of body measurements from individuals or their scans. |
| first_indexed | 2024-03-11T07:02:19Z |
| format | Article |
| id | doaj.art-302ec40a45fb44c9ba3dbec962458797 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T07:02:19Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-302ec40a45fb44c9ba3dbec9624587972023-11-17T09:09:26ZengMDPI AGAlgorithms1999-48932023-03-0116316110.3390/a16030161Human Body Shapes Anomaly Detection and Classification Using Persistent HomologySteve de Rose0Philippe Meyer1Frédéric Bertrand2Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10004 Troyes Cedex, FranceComputer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10004 Troyes Cedex, FranceComputer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10004 Troyes Cedex, FranceAccurate sizing systems of a population permit the minimization of the production costs of the textile apparel industry and allow firms to satisfy their customers. Hence, information about human body shapes needs to be extracted in order to examine, compare and classify human morphologies. In this paper, we use topological data analysis to study human body shapes. Persistence theory applied to anthropometric point clouds together with clustering algorithms show that relevant information about shapes is extracted by persistent homology. In particular, the homologies of human body points have interesting interpretations in terms of human anatomy. In the first place, anomalies of scans are detected using complete-linkage hierarchical clusterings. Then, a discrimination index shows which type of clustering separates gender accurately and if it is worth restricting to body trunks or not. Finally, Ward-linkage hierarchical clusterings with Davies–Bouldin, Dunn and Silhouette indices are used to define eight male morphotypes and seven female morphotypes, which are different in terms of weight classes and ratios between bust, waist and hip circumferences. The techniques used in this work permit us to classify human bodies and detect scan anomalies directly on the full human body point clouds rather than the usual methods involving the extraction of body measurements from individuals or their scans.https://www.mdpi.com/1999-4893/16/3/161topological data analysismachine learningpersistent homologyclusteringanomaly detectionmorphotype |
| spellingShingle | Steve de Rose Philippe Meyer Frédéric Bertrand Human Body Shapes Anomaly Detection and Classification Using Persistent Homology Algorithms topological data analysis machine learning persistent homology clustering anomaly detection morphotype |
| title | Human Body Shapes Anomaly Detection and Classification Using Persistent Homology |
| title_full | Human Body Shapes Anomaly Detection and Classification Using Persistent Homology |
| title_fullStr | Human Body Shapes Anomaly Detection and Classification Using Persistent Homology |
| title_full_unstemmed | Human Body Shapes Anomaly Detection and Classification Using Persistent Homology |
| title_short | Human Body Shapes Anomaly Detection and Classification Using Persistent Homology |
| title_sort | human body shapes anomaly detection and classification using persistent homology |
| topic | topological data analysis machine learning persistent homology clustering anomaly detection morphotype |
| url | https://www.mdpi.com/1999-4893/16/3/161 |
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