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Fast and Robust Segmentation of the Striatum Using Deep Convolutional Neural Networks

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Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

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 deployed ...

EPFL2024

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

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 ...

EPFL2024

Coronal jets identification using Deep Learning as Image and Video Object Detection

This report presents a study on the development and application of a Region-based Convolutional Neural Network, Faster RCNN and a more complex one, TransVOD, to locate solar coronal jets using data from the Solar Dynamic Observatory (SDO). The study focus ...

2024

DeepGeo: Deep Geometric Mapping for Automated and Effective Parameterization in Aerodynamic Shape Optimization

Pascal Fua, Zhen Wei

Aerodynamic shape optimization (ASO) is a key technique in aerodynamic designs, aimed at enhancing an object’s physical performance while adhering to specific constraints. Traditional parameterization methods for ASO often require substantial manual tuning ...

2024

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Jiancheng Yang, Li Zhang, Zhigang Li, Bo Du

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Cham2023

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Sahand Jamal Rahi, Kseniia Korchagina

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

Berlin2023

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This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

The Institute of Electrical and Electronics Engineers, Inc2023

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 ...

EPFL2023

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional eva ...

2023

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Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

Research Publishing2023