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During the detection process, obvious isolated outliers can be observed in the profile data; dimension parameter and profile-fitting accuracy are seriously affected by these outliers. In order to eliminate these outliers and improve the measurement accuracy, this paper uses Hampel identifier and moving mean identifier to identify isolated outliers. At the same time, the profile data is fitted, and the fitting results and fitting accuracy were analyzed and compared between the original data and the renovated data. The experiment proves that the outliers in the data must be identified and processed in the data measurement process. The Hampel identifier has better recognition effect, its algorithm is simple, efficient, and robust, and it can play an important role in the preprocessing of profile data based on structured light.<\/jats:p>","DOI":"10.1155\/2019\/3823691","type":"journal-article","created":{"date-parts":[[2019,7,21]],"date-time":"2019-07-21T19:30:36Z","timestamp":1563737436000},"page":"1-12","source":"Crossref","is-referenced-by-count":22,"title":["Using Hampel Identifier to Eliminate Profile-Isolated Outliers in Laser Vision Measurement"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1251-6058","authenticated-orcid":true,"given":"Zhibin","family":"Yao","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Taiyuan Heavy Machinery Equipment, Taiyuan 030024, China"},{"name":"Shanxi Provincial Key Laboratory of Metallurgical Device Design Theory and Technology, Taiyuan, 030024 Shanxi, China"},{"name":"College of Mechanical Engineering, 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