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Found 27 papers, 2 papers with code
QIS : Interactive Segmentation via Quasi-Conformal Mappings
In this paper, we propose the quasi-conformal interactive segmentation (QIS) model, which incorporates user input in the form of positive and negative clicks.
Enhancing Facial Classification and Recognition using 3D Facial Models and Deep Learning
Accurate analysis and classification of facial attributes are essential in various applications, from human-computer interaction to security systems.
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis
The QCTN is a deep neural network that outputs a quasiconformal map, which can be used to transform a geometrically distorted image into an improved version that is closer to the distribution of natural or good images.
Fast MRI Reconstruction via Edge Attention
To solve this problem, we propose a lightweight and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct images with edge guidance.
Topology-Preserving Segmentation Network
Comparing to the segmentation framework based on pixel-wise classification, deformation-based segmentation models that warp a template to enclose the regions are more convenient to enforce geometric constraints.
Automatic Landmark Detection and Registration of Brain Cortical Surfaces via Quasi-Conformal Geometry and Convolutional Neural Networks
In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks.
Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component
TPSN is a deformation-based model that yields a deformation map through a UNet, which takes the medical image and a template mask as inputs.
A unifying framework for $n$-dimensional quasi-conformal mappings
With the advancement of computer technology, there is a surge of interest in effective mapping methods for objects in higher-dimensional spaces.
A Deep Learning Framework for Diffeomorphic Mapping Problems via Quasi-conformal Geometry applied to Imaging
Existing methods to solve the mapping problems are often inefficient and can sometimes get trapped in local minima.
Topology-Preserving 3D Image Segmentation Based On Hyperelastic Regularization
In this paper, we propose a novel 3D topology-preserving registration-based segmentation model with the hyperelastic regularization, which can handle both 2D and 3D images.
Harmonic Beltrami Signature: A Novel 2D Shape Representation for Object Classification
The proposed signature is based on the harmonic extension of the conformal welding map of a unit circle and its Beltrami coefficient.
Modal Uncertainty Estimation via Discrete Latent Representations
Many important problems in the real world don't have unique solutions.
Quasiconformal model with CNN features for large deformation image registration
In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN).
Decomposition of Longitudinal Deformations via Beltrami Descriptors
To decompose the longitudinal deformation, we propose to carry out the low rank and sparse decomposition of the Beltrami descriptor.
Modal Uncertainty Estimation via Discrete Latent Representation
We motivate our use of discrete latent space through the multi-modal posterior collapse problem in current conditional generative models, then develop the theoretical background, and extensively validate our method on both synthetic and realistic tasks.
Shape analysis via inconsistent surface registration
In this work, we develop a framework for shape analysis using inconsistent surface mapping.
Tooth morphometry using quasi-conformal theory
We deploy our framework on a dataset of human premolars to analyze the tooth shape variation among genders and ancestries.
Subsampled Turbulence Removal Network
Instead of requiring a massive training sample size in deep networks, we purpose a training strategy that is based on a new data augmentation method to model turbulence from a relatively small dataset.
Variational models for joint subsampling and reconstruction of turbulence-degraded images
The energy consists of a fidelity term measuring the discrepancy between the extracted image and the subsampled frames, as well as regularization terms on the extracted image and the subsample.
Image retargeting via Beltrami representation
In this paper, we propose a simple and yet effective method to resize an image, which preserves the geometry of the important content, using the Beltrami representation.
Restoration of Atmospheric Turbulence-distorted Images via RPCA and Quasiconformal Maps
The subsampled image sequence is then stabilized by applying the Robust Principal Component Analysis (RPCA) on the deformation fields between image frames and warping the image frames by a quasiconformal map associated with the low-rank part of the deformation matrix.
Efficient Feature-based Image Registration by Mapping Sparsified Surfaces
For each high resolution image or video frame, we compute an optimal coarse triangulation which captures the important features of the image.
TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds
Based on the discrete analogue, we propose a novel method called TEMPO for computing Teichm\"{u}ller extremal mappings between feature-endowed point clouds.
Spherical Conformal Parameterization of Genus-0 Point Clouds for Meshing
In this paper, we extend a state-of-the-art spherical conformal parameterization algorithm for genus-0 closed meshes to the case of point clouds, using an improved approximation of the Laplace-Beltrami operator on data points.
Fast Disk Conformal Parameterization of Simply-connected Open Surfaces
Surface parameterizations have been widely used in computer graphics and geometry processing.
Surface Registration by Optimization in Constrained Diffeomorphism Space
The physically plausible constraints, in terms of feature landmarks and deformation types, define subspaces in the Beltrami coefficient space.
QCMC: Quasi-conformal Parameterizations for Multiply-connected domains
QCMC computes a quasi-conformal map from a multiply-connected domain $S$ onto a punctured disk $D_S$ associated with a given Beltrami differential.
