Shadow Detection
38 papers with code • 1 benchmarks • 3 datasets
SBU
SOBA
Latest papers with no code
Differentiable Point-based Inverse Rendering
To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering.
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
We developed a fully-automated deep learning method for FC segmentation.
AI driven shadow model detection in agropv farms
Agro-photovoltaic (APV) is a growing farming practice that combines agriculture and solar photovoltaic projects within the same area.
Video Instance Shadow Detection
First, we design SSIS-Track, a new framework to extract shadow-object associations in videos with paired tracking and without category specification; especially, we strive to maintain paired tracking even the objects/shadows are temporarily occluded for several frames.
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
Automated segmentation of microvessels in intravascular OCT images using deep learning
Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0. 71+/-0. 10 and pixel-wise sensitivity/specificity of 87. 7+/-6. 6%/99. 8+/-0. 1%.
Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection
The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability.
Learning Shadow Correspondence for Video Shadow Detection
We further design a new evaluation metric to evaluate the temporal stability of the video shadow detection results.
Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement
Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy.
Fine-Context Shadow Detection using Shadow Removal
First, we propose a Fine Context-aware Shadow Detection Network (FCSD-Net), where we constraint the receptive field size and focus on low-level features to learn fine context features better.
