no code implementations • 29 Feb 2024 • Lingfeng li, Xue-Cheng Tai, Raymond Chan
Unlike previous methods, our approach requires the predicted ABP waveforms to satisfy the Navier-Stokes equation with a time-periodic condition and a Windkessel boundary condition.
no code implementations • 31 Dec 2023 • Hao liu, Jun Liu, Raymond Chan, Xue-Cheng Tai
In this study, our goal is to integrate classical mathematical models with deep neural networks by introducing two novel deep neural network models for image segmentation known as Double-well Nets.
no code implementations • 25 Aug 2023 • Zhifang Liu, Baochen Sun, Xue-Cheng Tai, Qi Wang, Huibin Chang
A host of numerical experiments are conducted to show that the new algorithm produces good results with much-improved efficiency compared to other state-of-the-art algorithms for the EE model.
no code implementations • 18 Jul 2023 • Xue-Cheng Tai, Hao liu, Raymond Chan
We use the two-phase Potts model for image segmentation as an example for our explanations.
no code implementations • 18 Jul 2023 • Hao liu, Xue-Cheng Tai, Raymond Chan
In this paper, we give an algorithmic explanation for deep neural networks, especially in their connections with operator splitting.
no code implementations • 22 Mar 2022 • Shousheng Luo, Jinfeng Chen, Yunhai Xiao, Xue-Cheng Tai
This paper focuses on characterization methods for convex objects and applications in image processing.
no code implementations • 18 Mar 2022 • Hao liu, Xue-Cheng Tai, Ron Kimmel, Roland Glowinski
Recently, the authors proposed a color elastica model, which minimizes both the surface area and elastica of the image manifold.
no code implementations • 5 Mar 2022 • Yumeng Ren, Yiming Gao, Chunlin Wu, Xue-Cheng Tai
Because of the sparsity prior and deep unfolding method in the structure design, this IDmUNet combines the advantages of mathematical modeling and data-driven approaches.
no code implementations • 4 Aug 2021 • Hao liu, Xue-Cheng Tai, Roland Glowinski
In our method, we decouple the full nonlinearity of Gaussian curvature from differential operators by introducing two matrix- and vector-valued functions.
no code implementations • 6 Jul 2021 • Lingfeng li, Xue-Cheng Tai, Jiang Yang
We mainly focus on two regularization methods: the total variation and the Tikhonov regularization.
no code implementations • ICCV 2021 • Da Chen, Laurent D. Cohen, Jean-Marie Mirebeau, Xue-Cheng Tai
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
no code implementations • 28 Sep 2020 • Bin Wu, Xue-Cheng Tai, Talal Rahman
Three dimensional surface reconstruction based on two dimensional sparse information in the form of only a small number of level lines of the surface with moderately complex structures, containing both structured and unstructured geometries, is considered in this paper.
no code implementations • 24 Sep 2020 • Bin Wu, Leszek Marcinkowski, Xue-Cheng Tai, Talal Rahman
We propose a set of iterative regularization algorithms for the TV-Stokes model to restore images from noisy images with Gaussian noise.
no code implementations • 24 Sep 2020 • Bin Wu, Xue-Cheng Tai, Talal Rahman
A convergence analysis is given.
no code implementations • 24 Sep 2020 • Bin Wu, Xue-Cheng Tai, Talal Rahman
A complete multidimential TV-Stokes model is proposed based on smoothing a gradient field in the first step and reconstruction of the multidimensional image from the gradient field.
no code implementations • 19 Aug 2020 • Hao Liu, Xue-Cheng Tai, Ron Kimmel, Roland Glowinski
Here, we introduce an addition to the Polyakov action for color images that minimizes the color manifold curvature.
no code implementations • 15 May 2020 • Jun Liu, Xue-Cheng Tai, Shousheng Luo
This method is flexible and it can handle multiple objects and allow some of the objects to be convex.
no code implementations • 21 Mar 2020 • Lingfeng li, Shousheng Luo, Xue-Cheng Tai, Jiang Yang
We apply this new method to two applications: object segmentation with convexity prior and convex hull problem (especially with outliers).
no code implementations • 22 Feb 2020 • Shousheng Luo, Xue-Cheng Tai, Yang Wang
We present a novel and effective binary representation for convex shapes.
no code implementations • 10 Feb 2020 • Jun Liu, Xiangyue Wang, Xue-Cheng Tai
The novelty of our method is to interpret the softmax activation function as a dual variable in a variational problem, and thus many spatial priors can be imposed in the dual space.
no code implementations • 22 Sep 2019 • Haifeng Li, Jun Liu, Li Cui, Hai-yang Huang, Xue-Cheng Tai
Image segmentation with a volume constraint is an important prior for many real applications.
no code implementations • 9 Aug 2019 • Lingfeng Li, Shousheng Luo, Xue-Cheng Tai, Jiang Yang
In this model, the convex hull is characterized by the zero sublevel-set of a convex level set function, which is non-positive at every given point.
no code implementations • 28 Jun 2019 • Fan Jia, Jun Liu, Xue-Cheng Tai
That is, spatial regularity of the segmented objects is still a problem for CNNs.
no code implementations • 22 May 2018 • Shi Yan, Xue-Cheng Tai, Jun Liu, Hai-yang Huang
We apply our method to region and edge based level set segmentation models including Chan-Vese (CV) model with guarantee that the segmented region will be convex.
no code implementations • 28 Jul 2017 • Zachary Boyd, Egil Bae, Xue-Cheng Tai, Andrea L. Bertozzi
We show that modularity optimization is equivalent to minimizing a convex TV-based functional over a discrete domain, again, assuming the number of communities is known.
no code implementations • 26 Apr 2017 • Ke Wei, Ke Yin, Xue-Cheng Tai, Tony F. Chan
We propose an effective framework for multi-phase image segmentation and semi-supervised data clustering by introducing a novel region force term into the Potts model.
no code implementations • CVPR 2013 • Jing Yuan, Wu Qiu, Eranga Ukwatta, Martin Rajchl, Xue-Cheng Tai, Aaron Fenster
Segmenting 3D endfiring transrectal ultrasound (TRUS) prostate images efficiently and accurately is of utmost importance for the planning and guiding 3D TRUS guided prostate biopsy.