RESUMO
Non-rigid deformation of a template to fit 3D scans of human subjects is widely used to develop statistical models of 3D human shapes and poses. Complex optimization problems must be solved to use these models to parameterize scans of pregnant women, thus limiting their use in antenatal point-of-care tools in low-resource settings. Moreover, these models were developed using datasets that did not contain any 3D scans of pregnant women. In this study, we developed a statistical shape model of the torso of pregnant women at greater than 36 weeks of gestation using fast and simple vertex-based deformation of a cylindrical template constrained along the radial direction. The 3D scans were pre-processed to remove noisy outlier points and segment the torso based on anatomical landmarks. A cylindrical template mesh T was then fitted onto the segmented scan of the torso by moving each vertex of T in the direction of the radial vector. This process is computationally inexpensive taking only 14.80 seconds to deform a template with 9090 vertices. Principal component analysis (PCA) was performed on the deformed vertex co-ordinates to find the directions of maximum variance. The first 10 principal vectors of our model explained 79.03% of the total variance and reconstructed unseen scans with a mean error of 2.43 cm. We also used the PCA weights of the first 10 principal vectors to accurately predict anthropometric measurements of the pregnant women.
RESUMO
BACKGROUND: Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates. RESULTS: We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks. The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America. CONCLUSIONS: We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement. Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.