{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/model-order-selection-in-statistical-shape","title":"Model-order selection in statistical shape models","arxiv_id":"1808.00309","date":"2018-08-01","proceeding":null,"authors":["Alma Eguizabal","Peter J. Schreier","David Ramírez"],"abstract":"Statistical shape models enhance machine learning algorithms providing prior\ninformation about deformation. A Point Distribution Model (PDM) is a popular\nlandmark-based statistical shape model for segmentation. It requires choosing a\nmodel order, which determines how much of the variation seen in the training\ndata is accounted for by the PDM. A good choice of the model order depends on\nthe number of training samples and the noise level in the training data set.\nYet the most common approach for choosing the model order simply keeps a\npredetermined percentage of the total shape variation. In this paper, we\npresent a technique for choosing the model order based on information-theoretic\ncriteria, and we show empirical evidence that the model order chosen by this\ntechnique provides a good trade-off between over- and underfitting.","url_abs":"http://arxiv.org/abs/1808.00309v1","url_pdf":"http://arxiv.org/pdf/1808.00309v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"model-order-selection-in-statistical-shape","repo_url":"https://github.com/SSTGroup/Source-detection-in-colored-noise","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}