Shadow Detection
38 papers with code • 1 benchmarks • 3 datasets
SBU
SOBA
Latest papers with no code
Temporal Feature Warping for Video Shadow Detection
The current video shadow detection method achieves this goal via co-attention, which mostly exploits information that is temporally coherent but is not robust in detecting moving shadows and small shadow regions.
Mitigating Intensity Bias in Shadow Detection via Feature Decomposition and Reweighting
These two phenomenons reveal that deep shadow detectors heavily depend on the intensity cue, which we refer to as intensity bias.
Stereoscopic Flash and No-Flash Photography for Shape and Albedo Recovery
From the stereo image pair, we recover a rough shape that captures low-frequency shape variation without high-frequency details.
ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal
In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image.
Region Refinement Network for Salient Object Detection
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection.
Distraction-Aware Shadow Detection
In this paper, we propose a Distraction-aware Shadow Detection Network (DSDNet) by explicitly learning and integrating the semantics of visual distraction regions in an end-to-end framework.
New approach for solar tracking systems based on computer vision, low cost hardware and deep learning
In this work, a new approach for Sun tracking systems is presented.
A Reflectance Based Method For Shadow Detection and Removal
Shadows are common aspect of images and when left undetected can hinder scene understanding and visual processing.
Shadow Detection With Conditional Generative Adversarial Networks
We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images.
Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs).
