Population Shape Regression from Random Design Data

By Brad Davis, P. T. Fletcher, Elizabeth Bullitt, Sarang C. Joshi. IEEE International Conference on Computer Vision, 2007

325 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/163088df67df32b51aedded945195666db2ce8d0

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Geometry and Topology

Citations in the corpus

Wasserstein Regression (2020, background): “Options to address this include estimating cdfs (e.g., Aggarwal 1955; Read 1972; Falk 1983; Leblanc 2012), or estimating quantile functions (e.g., Parzen 1979; Falk 1984; Yang 1985; Cheng and Parzen 1997) of the underlying distributions.”
A dynamic 4D probabilistic atlas of the developing brain (2011, methodology): “This approach has been extended by Davis et al. (2007) to produce time-varying non-rigid templates using kernel-based regression.”
Geodesic Finite Mixture Models (2014, methodology): “For instance, they have been used to model shape changes in the brain [7], deformations of anatomical parts [10] and human motion [5, 32].”
Fréchet regression for random objects with Euclidean predictors (2016, methodology): “Local regression methods on generic metric spaces are limited to Nadaraya-Watson type estimators (Davis et al. 2007; Hein 2009; Steinke and Hein 2009; Steinke, Hein and Schölkopf 2010) and lack a comprehensive asymptotic analysis.”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, unknown): “Davis et al. (2007) proposed a method for constructing a timevarying non-rigid atlas using kernel regression.”
Learning Implicit Brain MRI Manifolds with Deep Learning (2018, background): “Many groups have sought to identify the best brain representation, such as building a template or atlas [2], linear mappings [3], Isomap [1], or regressions based on underlying parameters such as size or shape of regions of interest [4].”