1 code implementation • 28 Apr 2024 • Xinrun Chen, Mei Shen, Haojian Ning, Mengzhan Zhang, Chengliang Wang, Shiying Li
In this paper, we thus study how to use OCTA images with projection vascular layers to segment retinal structures.
no code implementations • 15 Mar 2024 • Mohammad Shifat E Rabbi, Naqib Sad Pathan, Shiying Li, Yan Zhuang, Abu Hasnat Mohammad Rubaiyat, Gustavo K Rohde
Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data.
no code implementations • 16 Oct 2023 • Shiying Li, Caroline Moosmueller
In particular, we demonstrate an invariance property with respect to the source measure, an equivariance property with respect to the target measure, and Lipschitz continuity concerning the slicing directions.
2 code implementations • 11 Oct 2023 • Xinrun Chen, Chengliang Wang, Haojian Ning, Shiying Li, Mei Shen
The method fine-tunes a pre-trained segment anything model (SAM) using low-rank adaptation (LoRA) and utilizes prompt points for local RVs, arteries, and veins segmentation in OCTA.
1 code implementation • 21 Sep 2023 • Chengliang Wang, Xinrun Chen, Haojian Ning, Shiying Li
In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary.
1 code implementation • 18 Sep 2023 • Haojian Ning, Chengliang Wang, Xinrun Chen, Shiying Li
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels.
no code implementations • ICCV 2023 • Wei Xie, Zimeng Zhao, Shiying Li, Binghui Zuo, Yangang Wang
Based on this representation, our Regional Unwrapping Transformer (RUFormer) learns the correlation priors across regions from monocular inputs and predicts corresponding contact and deformed transformations.
1 code implementation • 28 Jul 2023 • Le Gong, Shiying Li, Naqib Sad Pathan, Mohammad Shifat-E-Rabbi, Gustavo K. Rohde, Abu Hasnat Mohammad Rubaiyat, Sumati Thareja
Here we describe a new image representation technique based on the mathematics of transport and optimal transport.
no code implementations • 11 Jul 2023 • Shiying Li, Caroline Moosmueller
The proof builds on an interpretation as a stochastic gradient descent scheme on the Wasserstein space.
no code implementations • 21 Apr 2023 • Binbin Huang, Xingyue Peng, Siyuan Shen, Suan Xia, Ruiqian Li, Yanhua Yu, Yuehan Wang, Shenghua Gao, Wenzheng Chen, Shiying Li, Jingyi Yu
The core of our method is to put the object nearby diffuse walls and augment the LOS scan in the front view with the NLOS scans from the surrounding walls, which serve as virtual ``mirrors'' to trap lights toward the object.
no code implementations • 4 Apr 2023 • Wuwei Ren, Siyuan Shen, Linlin Li, Shengyu Gao, Yuehan Wang, Liangtao Gu, Shiying Li, Xingjun Zhu, Jiahua Jiang, Jingyi Yu
Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media, such as biological tissues and foggy air.
no code implementations • ICCV 2023 • Yanhua Yu, Siyuan Shen, Zi Wang, Binbin Huang, Yuehan Wang, Xingyue Peng, Suan Xia, Ping Liu, Ruiqian Li, Shiying Li
Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem.
no code implementations • 4 Jun 2022 • Shiying Li, Abu Hasnat Mohammad Rubaiyat, Gustavo K. Rohde
Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present.
1 code implementation • 30 Apr 2022 • Abu Hasnat Mohammad Rubaiyat, Shiying Li, Xuwang Yin, Mohammad Shifat E Rabbi, Yan Zhuang, Gustavo K. Rohde
This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT).
1 code implementation • 22 Feb 2022 • Yan Zhuang, Shiying Li, Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Gustavo K. Rohde
Face recognition is then performed using a nearest subspace in R-CDT domain of local gradient distributions.
2 code implementations • 9 Jan 2022 • Mohammad Shifat E Rabbi, Yan Zhuang, Shiying Li, Abu Hasnat Mohammad Rubaiyat, Xuwang Yin, Gustavo K. Rohde
However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective.
no code implementations • 29 Dec 2021 • Zhengqing Pan, Ruiqian Li, Tian Gao, Zi Wang, Ping Liu, Siyuan Shen, Tao Wu, Jingyi Yu, Shiying Li
There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle.
1 code implementation • 11 Oct 2021 • Abu Hasnat Mohammad Rubaiyat, Mohammad Shifat-E-Rabbi, Yan Zhuang, Shiying Li, Gustavo K. Rohde
This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT).
1 code implementation • 2 Jan 2021 • Siyuan Shen, Zi Wang, Ping Liu, Zhengqing Pan, Ruiqian Li, Tian Gao, Shiying Li, Jingyi Yu
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
1 code implementation • 11 Dec 2020 • Xuwang Yin, Shiying Li, Gustavo K. Rohde
We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT).
no code implementations • 8 Aug 2020 • Akram Aldroubi, Shiying Li, Gustavo K. Rohde
A relatively new set of transport-based transforms (CDT, R-CDT, LOT) have shown their strength and great potential in various image and data processing tasks such as parametric signal estimation, classification, cancer detection among many others.
3 code implementations • 7 Apr 2020 • Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Shiying Li, Soheil Kolouri, Akram Aldroubi, Jonathan M. Nichols, Gustavo K. Rohde
We present a new supervised image classification method applicable to a broad class of image deformation models.
no code implementations • 7 Mar 2019 • Yuanxi Ma, Cen Wang, Shiying Li, Jingyi Yu
Robust segmentation of hair from portrait images remains challenging: hair does not conform to a uniform shape, style or even color; dark hair in particular lacks features.