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Found 12 papers, 3 papers with code
Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data
Once trained, SHMGAN is able to generate specular-free images from a single RGB image as input; without requiring any additional external labels.
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection.
SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images
Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest.
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips.
Physically-admissible polarimetric data augmentation for road-scene analysis
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis.
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
Towards urban scenes understanding through polarization cues
Autonomous robotics is critically affected by the robustness of its scene understanding algorithms.
P2D: a self-supervised method for depth estimation from polarimetry
Monocular depth estimation is a recurring subject in the field of computer vision.
Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model
The proposed model could be used for the automatic segmentation of myocardial border that is a very important step for accurate quantification of no-reflow, myocardial infarction, myocarditis, and hypertrophic cardiomyopathy, among others.
Polarimetric image augmentation
Robotics applications in urban environments are subject to obstacles that exhibit specular reflections hampering autonomous navigation.
Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning
The efficiency of the proposed method is mostly due to the high power of the polarimetry to discriminate any object by its reflective properties and on the use of deep neural networks for object detection.
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech
The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accuracy of 76. 27% and a root mean square error of 0. 4 in assessing depression, while a root mean square error of 0. 168 is achieved in predicting the depression severity levels.
