1,746 research outputs found
A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching
We propose a combinatorial solution for the problem of non-rigidly matching a
3D shape to 3D image data. To this end, we model the shape as a triangular mesh
and allow each triangle of this mesh to be rigidly transformed to achieve a
suitable matching to the image. By penalising the distance and the relative
rotation between neighbouring triangles our matching compromises between image
and shape information. In this paper, we resolve two major challenges: Firstly,
we address the resulting large and NP-hard combinatorial problem with a
suitable graph-theoretic approach. Secondly, we propose an efficient
discretisation of the unbounded 6-dimensional Lie group SE(3). To our knowledge
this is the first combinatorial formulation for non-rigid 3D shape-to-image
matching. In contrast to existing local (gradient descent) optimisation
methods, we obtain solutions that do not require a good initialisation and that
are within a bound of the optimal solution. We evaluate the proposed method on
the two problems of non-rigid 3D shape-to-shape and non-rigid 3D shape-to-image
registration and demonstrate that it provides promising results.Comment: 10 pages, 7 figure
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has
been widely used for synthesis of multi-domain medical images. The
domain-specific nonlinear deformations captured by CycleGAN make the
synthesized images difficult to be used for some applications, for example,
generating pseudo-CT for PET-MR attenuation correction. This paper presents a
deformation-invariant CycleGAN (DicycleGAN) method using deformable
convolutional layers and new cycle-consistency losses. Its robustness dealing
with data that suffer from domain-specific nonlinear deformations has been
evaluated through comparison experiments performed on a multi-sequence brain MR
dataset and a multi-modality abdominal dataset. Our method has displayed its
ability to generate synthesized data that is aligned with the source while
maintaining a proper quality of signal compared to CycleGAN-generated data. The
proposed model also obtained comparable performance with CycleGAN when data
from the source and target domains are alignable through simple affine
transformations
Learning based automatic face annotation for arbitrary poses and expressions from frontal images only
Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases
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