Search Results for author:
Found 22 papers, 11 papers with code
Linear optimal transport subspaces for point set classification
Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data.
Approximation properties of slice-matching operators
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.
SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation
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.
SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks
In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary.
An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels.
Nonrigid Object Contact Estimation With Regional Unwrapping Transformer
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.
The Radon Signed Cumulative Distribution Transform and its applications in classification of Signed Images
Here we describe a new image representation technique based on the mathematics of transport and optimal transport.
Measure transfer via stochastic slicing and matching
The proof builds on an interpretation as a stochastic gradient descent scheme on the Wasserstein space.
Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction
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.
High-resolution tomographic reconstruction of optical absorbance through scattering media using neural fields
Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media, such as biological tissues and foggy air.
Enhancing Non-line-of-sight Imaging via Learnable Inverse Kernel and Attention Mechanisms
Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem.
Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis
Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present.
End-to-End Signal Classification in Signed Cumulative Distribution Transform Space
This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT).
Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition
Face recognition is then performed using a nearest subspace in R-CDT domain of local gradient distributions.
Invariance encoding in sliced-Wasserstein space for image classification with limited training data
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.
Onsite Non-Line-of-Sight Imaging via Online Calibrations
There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle.
Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification
This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT).
Non-line-of-Sight Imaging via Neural Transient Fields
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
Learning Energy-Based Models With Adversarial Training
We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT).
Partitioning signal classes using transport transforms for data analysis and machine learning
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.
Radon cumulative distribution transform subspace modeling for image classification
We present a new supervised image classification method applicable to a broad class of image deformation models.
Hair Segmentation on Time-of-Flight RGBD Images
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.
