Abstract
Abstract. Many anthropological researches require identification and measurement of craniometric and cephalometric landmarks which provide valuable information about the shape of a head. This information is necessary for morphometric analysis, face approximation, craniafacial identification etc. Traditional techniques use special anthropological tools to perform required measurements, identification of landmarks usually being made by an expert-anthropologist. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the deep learning technique for anthropological landmarks identification and accurate 3D measurements. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.
Highlights
Object’s visual and geometrical characteristics serve as essential data sources in anthropological and paleoanthropological studies
The progress in 3D acquisition techniques creates the background for introducing in practice of anthropological research accurate 3D models of anthropological objects, allowing to perform accurate measurements in separate points, but to carry out complicated morphological analysis of an object
The paper presents the deep learning technique for recognition of the craniometric landmarks, that are used for morphometric anthropological research
Summary
Object’s visual and geometrical characteristics serve as essential data sources in anthropological and paleoanthropological studies. Sophisticated mechanical tools has been specially designed for analysis of object morphology. Such instruments as sliding caliper (Martin type), coordinate caliper (Aichel type), spreading caliper, craniofor (Mollison type) and mandibulometer are usually exploited for obtaining specific craniometric parameters. Measurements and analysis of linear, angular and shape parameters allow to make decisions on paleoanthropological characteristics of an object. The paper presents the deep learning technique for recognition of the craniometric landmarks, that are used for morphometric anthropological research. The performed study addresses to a problem of craniometric landmarks recognition in skull 2D images
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
