Estimating average growth trajectories in shape-space using kernel smoothing.
IEEE Trans Med Imaging
In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.
|Title:||Estimating average growth trajectories in shape-space using kernel smoothing.|
|Keywords:||Adolescent, Adult, Aging, Algorithms, Cephalometry, Child, Child, Preschool, Face, Facies, Female, Head, Humans, Imaging, Three-Dimensional, Infant, Infant, Newborn, Male, Maxillofacial Development, Middle Aged, Models, Biological, Pattern Recognition, Automated, Sex Factors, Subtraction Technique|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Child Health
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health Care > CHIME
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