Current point cloud extraction methods based on photogrammetry generate large amounts of spurious detections that hamper useful 3D mesh reconstructions or, even worse, the possibility of adequate measurements. Moreover, noise removal methods for point clouds are complex, slow and incapable to cope with semantic noise. In this work, we present body2vec, a model-based body segmentation tool that uses a specifically trained Neural Network architecture. Body2vec is capable to perform human body point cloud reconstruction from videos taken on hand-held devices (smartphones or tablets), achieving high quality anthropometric measurements. The main contribution of the proposed workflow is to perform a background removal step, thus avoiding the spurious points generation that is usual in photogrammetric reconstruction. A group of 60 persons were taped with a smartphone, and the corresponding point clouds were obtained automatically with standard photogrammetric methods. We used as a 3D silver standard the clean meshes obtained at the same time with LiDAR sensors post-processed and noise-filtered by expert anthropological biologists. Finally, we used as gold standard anthropometric measurements of the waist and hip of the same people, taken by expert anthropometrists. Applying our method to the raw videos significantly enhanced the quality of the results of the point cloud as compared with the LiDAR-based mesh, and of the anthropometric measurements as compared with the actual hip and waist perimeter measured by the anthropometrists. In both contexts, the resulting quality of body2vec is equivalent to the LiDAR reconstruction.
Objectives The increased availability of genome‐wide data allows capturing the fine genetic structure of present days populations. Here we analyze the genetic ancestry at a fine scale of an Argentinean Patagonia population to understand the origins beyond the three‐hybrid model, and to compare these results with volunteers' self‐perceived ancestry in a broad context encompassed by historical and familiar information. Materials and Methods We compare high‐throughput genotyping data for 92 individuals that we generated to data sets from the literature by applying fully haplotype‐based methods to examine patterns of human population substructure. The volunteers filled out a semi‐structured questionnaire, including questions about their history, ancestors, and self‐perceived ancestry. Finally, we used non‐parametric tests in order to compare genomic ancestry against self‐perception. Results Genetic ancestry from Iberian populations accounted for 0.176 (Spain and Basque origins), while the component associated with Italian populations accounted for 0.140. We observed a 0.169 Native American genetic ancestry. Participants significantly over‐ and under‐ self‐perceived Native American and European origins, respectively. Components of origins from North Africa to Central South Asia accounted for 0.225 of the genetic ancestry in the sample, with significantly higher proportions for people that mentioned such origins in their genealogical history. Discussion We captured the fine‐genetic architecture of a Puerto Madryn population sample in Chubut province, showing that self‐perceived ancestry remains a poor proxy for genetic ancestry. The presence of North Africa to Central South Asia components and its correlate with self‐perception of these origins justifies its inclusion in future miscegenation studies in Argentina.
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