Statistical models of appearance for medical image analysis and computer vision

By Tim Cootes, Christopher J. Taylor. SPIE Medical Imaging, 2001

539 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/79773172722241baacbb2071240fd28775e7c65f

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In Computer Vision and Pattern Recognition

Citations in the corpus

Review of statistical shape spaces for 3D data with comparative analysis for human faces (2012, methodology): “We expect that, like part-based models, the local wavelet model will obtain locally more accurate results than the global model, since the basis functions have limited extent.”
Automatic segmentation of echocardiographic sequences by active appearance motion models (2002, methodology): “[17], [18] and forms an extension of the widely applied ASMs [13] from which it inherited the shape-modeling approach.”
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors (2008, methodology): “We then extend the model to model temporal dynamics in object shape, allowing the algorithm to robustly handle severe cases of missing data.”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, methodology): “Many works build upon the framework of Morphable Models [38] or Active Appearance Models (AAMs) [15, 16], in which statistical models of shape and texture are constructed.”
FAME-a flexible appearance modeling environment (2003, methodology): “in single-slice magnetic resonance images (MRI) of the brain. This case was also treated in [ 7 ] and [8]”
A Bayesian model of shape and appearance for subcortical brain segmentation (2011, unknown): “This is in contrast to the weightings between forces or shape–intensity relations, as used by other approaches (Cootes and Taylor, 2001; Heimann and Meinzer, 2009; Lobregt and Viergever, 1995; Montagnat et al., 2001; Pitiot et al., 2004; Shen et al., 2011), as they typically have a significant…”
Interactive graph cut based segmentation with shape priors (2005, background): “[5], which has been successfully applied to the threedimensional segmentation of medical volumes, including magnetic resonance images of the brain, heart, and articular cartilage [9, 12, 20].”
Map-based exploration of intrinsic shape differences and variability (2013, background): “In computer graphics, as shape collections are getting larger and larger with more objects in each category, these finer and more detailed shape comparisons are becoming important – and difficult to handle by coarse traditional techniques.”
Parametrized shape models for clothing (2011, methodology): “Related models for fully non-rigid shape modeling and estimation are typically based on a learned manifold, e.g., active appearance models [ 5 ].”
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces (2008, methodology): “three based on Active Appearance Model (AAM) [16] and one based”