The Euler Elastica (EE) model with surface curvature can generate
artifa...
Deep neural network is a powerful tool for many tasks. Understanding why...
For problems in image processing and many other fields, a large class of...
Solution methods for the nonlinear partial differential equation of the
...
In this work, we derive a priori error estimate of the mixed residual me...
This paper focuses on the issue of image segmentation with convex shape
...
One classical approach to regularize color is to tream them as two
dimen...
UNet and its variants are among the most popular methods for medical ima...
Gaussian curvature is an important geometric property of surfaces, which...
We study gradient-based regularization methods for neural networks. We m...
In ptychography experiments, redundant scanning is usually required to
g...
Three dimensional surface reconstruction based on two dimensional sparse...
We propose a set of iterative regularization algorithms for the TV-Stoke...
The paper presents a fully coupled TV-Stokes model, and propose an algor...
A complete multidimential TV-Stokes model is proposed based on smoothing...
Models related to the Euler's Elastica energy have proven to be very use...
Convex Shapes (CS) are common priors for optic disc and cup segmentation...
In this work, we present a new efficient method for convex shape
represe...
We present a novel and effective binary representation for convex shapes...
We use Deep Convolutional Neural Networks (DCNNs) for image segmentation...
Image segmentation with a volume constraint is an important prior for ma...
Seeking the convex hull of an object is a very fundamental problem arisi...
We propose a geometric convexity shape prior preservation method for
var...
Networks capture pairwise interactions between entities and are frequent...