Search Results for author:
Found 25 papers, 10 papers with code
CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric Insights
By incorporating FMs, we can capture the subtle tangential movements of the myocardium surface precisely, hence significantly improving the accuracy of 3D mapping of the myocardium.
Synthetic Data Generation for 3D Myocardium Deformation Analysis
Our work contributes to overcoming the limitations imposed by the scarcity of high-resolution CT datasets with precise annotations, thereby facilitating the development of accurate and reliable myocardium deformation analysis algorithms for clinical applications and diagnostics.
Temporal Super-Resolution using Multi-Channel Illumination Source
In this work we try to increase the temporal resolution beyond the Nyquist frequency, which is limited by the sampling rate of the sensor.
Illumination-Based Color Reconstruction for the Dynamic Vision Sensor
This work demonstrates a novel, state of the art method to reconstruct colored images via the Dynamic Vision Sensor (DVS).
Deep Confidence Guided Distance for 3D Partial Shape Registration
We present a novel non-iterative learnable method for partial-to-partial 3D shape registration.
Shape-consistent Generative Adversarial Networks for multi-modal Medical segmentation maps
Our goal is to use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction.
Ranked #4 on
3D Dense Shape Correspondence
on SHREC'19
Deep Weighted Consensus: Dense correspondence confidence maps for 3D shape registration
We present a new paradigm for rigid alignment between point clouds based on learnable weighted consensus which is robust to noise as well as the full spectrum of the rotation group.
Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds
Understanding the flow in 3D space of sparsely sampled points between two consecutive time frames is the core stone of modern geometric-driven systems such as VR/AR, Robotics, and Autonomous driving.
Unsupervised Scale-Invariant Multispectral Shape Matching
Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled data for real datasets.
Geometry Enhancements from Visual Content: Going Beyond Ground Truth
This work presents a new cyclic architecture that extracts high-frequency patterns from images and re-insert them as geometric features.
Skeleon-Based Typing Style Learning For Person Identification
We present a novel architecture for person identification based on typing-style, constructed of adaptive non-local spatio-temporal graph convolutional network.
It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection
Modern perception systems in the field of autonomous driving rely on 3D data analysis.
Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment
We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities.
Occlusion Guided Scene Flow Estimation on 3D Point Clouds
3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors.
Cost Function Unrolling in Unsupervised Optical Flow
Replacing the L1 smoothness constraint with our unrolled cost during the training of a well known baseline, we report improved results on both MPI Sintel and KITTI 2015 unsupervised optical flow benchmarks.
Ranked #14 on
Optical Flow Estimation
on KITTI 2015
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision.
Rolling Shutter Correction
Self-supervised Scene Flow Estimation
MRZ code extraction from visa and passport documents using convolutional neural networks
Detecting and extracting information from Machine-Readable Zone (MRZ) on passports and visas is becoming increasingly important for verifying document authenticity.
Optical Character Recognition
Optical Character Recognition (OCR)
OCR Graph Features for Manipulation Detection in Documents
Detecting manipulations in digital documents is becoming increasingly important for information verification purposes.
Optical Character Recognition
Optical Character Recognition (OCR)
Printing and Scanning Attack for Image Counter Forensics
Of the three models, our proposed model outperforms the others when trained and validated on images from a single printer.
Cyclic Functional Mapping: Self-supervised correspondence between non-isometric deformable shapes
We present the first utterly self-supervised network for dense correspondence mapping between non-isometric shapes.
Multi-velocity neural networks for gesture recognition in videos
We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification.
Deep video gesture recognition using illumination invariants
In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling.
Coreset-Based Adaptive Tracking
We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment.
