Active shape models—their training and application

TL;DR: This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations.

TL;DR: This study marks, to the authors' knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.

TL;DR: It is shown that this momentum can be also used for describing a deformation of given visual structures, like points, contours or images, and that, it has the same dimension as the described object, as a consequence of the normal momentum constraint the authors introduce.

TL;DR: A novel approach to measuring similarity between shapes and exploiting it for object recognition in a nearest-neighbor classification framework that applies regularized thin-plate splines to the transformation maps for this purpose.

TL;DR: Current methods of parameter solving are extended to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulation, variable dimensions, or surface deformations to allow model-based vision to be used for a much wider class of problems than was possible with previous methods.

TL;DR: A physically-based approach is presented to fitting complex 3D shapes using a novel class of dynamic models which incorporate the global shape parameters of a conventional superellipsoid with the local degrees of freedom of a spline.

TL;DR: It is described how the models can be used in local image search and give examples of their application to medical images, and how the method can be simply extended to segment 3-D objects in volume images and to track structures in image sequences.

TL;DR: The authors present a closed-form, physically based solution for recovering a three-dimensional (3-D) solid model from collections of 3-D surface measurements that is overconstrained and unique except for rotational symmetries.