3D SfM as a measuring technique for human body transformation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11401 LNCS, Page: 150-158
2019
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Conference Paper Description
The tracking of fat loss as well as muscle gain has always been one of the most important steps during a person’s fitness journey. It does not only motivate to continue practicing exercises, but also helps to develop specific workout plans to enhance particular body parts of athletes. Structure for Motion (SfM), unlike other reconstruction techniques, produces acceptable results from low-quality inputs. This makes the method applicable for ubiquitous equipment like a smartphone camera, while still being scalable to professional environments with proper equipment. In order to track overall body transformation, we propose a photogrammetry workflow employing SfM, reproducibly generating a model of the human body in different stages of a fitness plan. For visualization, we do a mesh alignment step followed by a comparison between the reconstructed body models of the subject, resulting in color-mapped meshes. Following this workflow the transformation of specific body regions can be analyzed in detail, only using consumer hardware.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85063053107&origin=inward; http://dx.doi.org/10.1007/978-3-030-13469-3_18; https://link.springer.com/10.1007/978-3-030-13469-3_18; https://dx.doi.org/10.1007/978-3-030-13469-3_18; https://link.springer.com/chapter/10.1007/978-3-030-13469-3_18
Springer Science and Business Media LLC
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