Efficient Shape Priors for Spline-Based Snakes

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Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Gene regulatory networks (GRNs) play a crucial role in an organism's response to changing environmental conditions. Cellular behaviors typically result from the integration of multiple gene outputs, and their regulation often demands precise control of num ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Time-resolved non-line-of-sight (NLOS) imaging based on single-photon avalanche diode (SPAD) detectors have demonstrated impressive results in recent years. To acquire adequate number of indirect photons from a hidden scene in the presence of overwhelming ...

Nanometric scale size oscillations seem to be a fundamental feature of all living organisms on Earth. Their detection usually requires complex and very sensitive devices. However, some recent studies demonstrated that very simple optical microscopes and de ...

IntroductionPatients undergoing cancer treatment by radiation therapy commonly develop Candida albicans infections (candidiasis). Such infections are generally treated by antifungals that unfortunately also induce numerous secondary effects in the patient. ...

The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Accurate real-time estimation of the gait phase (GP) is crucial for many control methods in exoskeletons and prostheses. A class of approaches to GP estimation construct the phase portrait of a segment or joint angle, and use the normalized polar angle of ...

Determining the relationship between the cause of damage and the subsequent structural behavior of infrastructure systems requires an accurate characterization of the propagation of cracks, which represents the evolution of the damage state. When no inform ...

We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides the RGB ...

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on bot ...

In this article, we propose a novel approach for the localization of electromagnetic interference (EMI) sources based on the concept of compressive sensing and metalenses. We show that using the spatial and frequency dispersion offered by the metalens, we ...

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system and affects almost 3 million people worldwide. There is currently no cure for MS, and its symptoms, starting with fatigue and weakness, often progress over time ...

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

Efficient Shape Priors for Spline-Based Snakes

Chattez avec Graph Search

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Quantitative characterization of the inorganic phosphate gene regulatory networks in S. cerevisiae and bottom up engineering an orthogonal gene network

Aggregating Spatial and Photometric Context for Photometric Stereo

The multimodality cell segmentation challenge: toward universal solutions

A Gradient-Gated SPAD Array for Non-Line-of-Sight Imaging

Mitochondrial nanomotion measured by optical microscopy

Modulation of the nanoscale motion rate of Candida albicans by X-rays

Learning Transformations To Reduce the Geometric Shift in Object Detection

Detecting Anomalies and Obstacles in Road Scenes

Confidence Matters: Applications to Semantic Segmentation

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Gait Phase Estimation in Steady Walking: A Comparative Study of Methods Based on the Phase Portrait of the Hip Angle

Determining crack kinematics from imaged crack patterns

MultiMAE: Multi-modal Multi-task Masked Autoencoders

Object Priors for Volumetric Image Segmentation

Equivariant Diffusion for Molecule Generation in 3D

A Compressive Sensing Framework for EMI Source Localization Using a Metalens Structure: Localization Beyond the Diffraction Limit

Attribute Prediction as Multiple Instance Learning

Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI

Improving the Training of Compact Neural Networks for Visual Recognition