Pinned Repositories
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
avatree
This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making.
awesome-iclr-2024
camera-shake-removal-implementation-android
Android implementation for motion deblurring and camera shake removal
deep-saliency-mapping
fast-mesh-denoising
This is an implementation for the paper entitled "Fast mesh denoising with data driven normal filtering using deep variational autoencoders" published in IEEE Transactions on Industrial Informatics 10.1109/TII.2020.3000491
mesh-saliency-detection-using-convolutional-neural-networks
Mesh saliency has been widely considered as the measure of visual importance of certain parts of 3D geometries, distinguishable from their surroundings, with respect to human visual perception. This work is based on the use of convolutional neural networks to extract saliency maps for large and dense 3D scanned models. The network is trained with saliency maps extracted by fusing local and global spectral characteristics. Extensive evaluation studies carried out using various 3D models, include visual perception evaluation in simplification and compression use cases. As a result, they verify the superiority of our approach as compared to other state-of-the-art approaches. Furthermore, these studies indicate that CNN-based saliency extraction method is much faster in large and dense geometries, allowing the application of saliency aware compression and simplification schemes in low-latency and energy-efficient systems.
Multimodal-fusion-driven-automotive-scene-analysis
This repository provides a plugin for the OpenPCDet object detection framework that facilitates fusion of 2D and 3D object detection.
Revisiting-Content-Based-Audio-Classification-for-Asthma-Medication-Adherence
Asthma is a common, usually long-term respiratory disease with negative impact on society and the economy worldwide. Treatment involves using medical devices (inhalers) that distribute medicationto the airways, and its efficiency depends on the precision of the inhalation technique. Health monitoring systems equipped with sensors and embedded with sound signal detection enable the recognition of drug actuation and could be powerful tools for reliable audio content analysis. This repository includes a set of tools for audio processing, feature extraction and classification and is provided along with a dataset consisting of respiratory and drug actuation sounds. The classification models are implemented based on machine learning and deep approaches. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses challenges and future tendencies.
Robust-and-fast-3-D-saliency-mapping
This repository contains an implementation of "Robust and fast 3-D saliency mapping" proposed by Gerasimos Arvanitis, Aris Lalos and konstantinos Moustakas. The presented method was implemented in python by Stavros Nousias
snousias's Repositories
snousias/mesh-saliency-detection-using-convolutional-neural-networks
Mesh saliency has been widely considered as the measure of visual importance of certain parts of 3D geometries, distinguishable from their surroundings, with respect to human visual perception. This work is based on the use of convolutional neural networks to extract saliency maps for large and dense 3D scanned models. The network is trained with saliency maps extracted by fusing local and global spectral characteristics. Extensive evaluation studies carried out using various 3D models, include visual perception evaluation in simplification and compression use cases. As a result, they verify the superiority of our approach as compared to other state-of-the-art approaches. Furthermore, these studies indicate that CNN-based saliency extraction method is much faster in large and dense geometries, allowing the application of saliency aware compression and simplification schemes in low-latency and energy-efficient systems.
snousias/avatree
This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making.
snousias/fast-mesh-denoising
This is an implementation for the paper entitled "Fast mesh denoising with data driven normal filtering using deep variational autoencoders" published in IEEE Transactions on Industrial Informatics 10.1109/TII.2020.3000491
snousias/camera-shake-removal-implementation-android
Android implementation for motion deblurring and camera shake removal
snousias/deep-saliency-mapping
snousias/Revisiting-Content-Based-Audio-Classification-for-Asthma-Medication-Adherence
Asthma is a common, usually long-term respiratory disease with negative impact on society and the economy worldwide. Treatment involves using medical devices (inhalers) that distribute medicationto the airways, and its efficiency depends on the precision of the inhalation technique. Health monitoring systems equipped with sensors and embedded with sound signal detection enable the recognition of drug actuation and could be powerful tools for reliable audio content analysis. This repository includes a set of tools for audio processing, feature extraction and classification and is provided along with a dataset consisting of respiratory and drug actuation sounds. The classification models are implemented based on machine learning and deep approaches. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses challenges and future tendencies.
snousias/Robust-and-fast-3-D-saliency-mapping
This repository contains an implementation of "Robust and fast 3-D saliency mapping" proposed by Gerasimos Arvanitis, Aris Lalos and konstantinos Moustakas. The presented method was implemented in python by Stavros Nousias
snousias/academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
snousias/awesome-iclr-2024
snousias/Multimodal-fusion-driven-automotive-scene-analysis
This repository provides a plugin for the OpenPCDet object detection framework that facilitates fusion of 2D and 3D object detection.
snousias/awesome-point-cloud-analysis-2020
A list of papers and datasets about point cloud analysis (processing) since 2017. Update every day!
snousias/awesome-public-datasets
A topic-centric list of HQ open datasets.
snousias/datasets
Datasets used in Plotly examples and documentation
snousias/Datasets-1
Machine learning datasets used in tutorials on MachineLearningMastery.com
snousias/datasets-2
A collection of datasets of ML problem solving
snousias/mathvista.github.io
Website for MathVista
snousias/nerfies.github.io
snousias/nlp-datasets
Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)
snousias/PageTemplate
snousias/promptpg.github.io
snousias/robust-pca
A simple Python implementation of R-PCA