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Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"
Related publications (32)
Deploying an Instance Segmentation Algorithm to Implement Social Distancing for Prosthetic Vision
Silvestro Micera, Daniela De Luca
The COVID-19 pandemic outbreak is causing a dramatic worsening in the already complicated living conditions of blind and visually impaired individuals. Social distancing is the most effective strategy to limit virus spread, but is extremely difficult for b ...
IEEE2022Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets
Jean-Marc Odobez, Rémy Alain Siegfried
Visual Focus of Attention (VFOA) estimation in conversation is challenging as it relies on difficult to estimate information (gaze) combined with scene features like target positions and other contextual information (speaking status) allowing to disambigua ...
IEEE COMPUTER SOC2021Deep Learning‐based Semantic Segmentation in Remote Sensing
Semantic segmentation consists of the generation of a categorical map, given an image in which each pixel of the image is automatically assigned a class. Deep learning allows the influence of the pixel's context to be learned by capturing the non-linear re ...
Wiley2021Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation
Touradj Ebrahimi, Pinar Akyazi
Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...
SPIE-INT SOC OPTICAL ENGINEERING2021The role of convolutional neural networks in scanning probe microscopy: a review
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials desi ...
BEILSTEIN-INSTITUT2021Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias
Alexandre Schmid, Lizeth Gonzalez Carabarin
Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Th ...
IEEE2021Automation of the diagnosis process in the railway system : Detection of defects in concrete sleepers using vision-based machine learning models
inspectors that walk over the track and check the defects on the rail surface, fasteners and sleepers. In the case of concrete sleepers, rail inspectors classify defects according to their size and occurrence over 20 sleepers. The manual inspection is erro ...
2021DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
Michaël Unser, Daniel Sage, Laurène Donati
DeepImageJ offers a user-friendly solution in ImageJ to run trained deep learning models for biomedical image analysis. It includes guiding tools for reliable analyses, contributing to the democratization of deep learning in microscopy. DeepImageJ is a use ...
NATURE PORTFOLIO2021Vision based pixel-level bridge structural damage detection using a link ASPP network
Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface s ...
2020Comparison of crack segmentation using digital image correlation measurements and deep learning
Katrin Beyer, Radhakrishna Achanta, Amir Rezaie, Michele Godio
Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on di ...
2020