Object Priors for Volumetric Image Segmentation

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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 ...

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Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

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 ...

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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 ...

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The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Object Priors for Volumetric Image Segmentation

Chattez avec Graph Search

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Incorporating Projective Geometry into Deep Learning

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

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

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Robust machine learning for neuroscientific inference

Enabling Uncertainty Estimation in Iterative Neural Networks

Robust NAS under adversarial training: benchmark, theory, and beyond

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The multimodality cell segmentation challenge: toward universal solutions

Machine Learning for Modeling Stock Returns

Random matrix methods for high-dimensional machine learning models

Digital Twins of Stone Masonry Buildings for Damage Assessment

Towards Trustworthy Deep Learning for Image Reconstruction

Quartiers rhodaniens en transition

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination