Results 1 to 10 of about 4,979,718 (211)
Detect Any Shadow: Segment Anything for Video Shadow Detection [PDF]
IEEE Transactions on Circuits and Systems for Video Technology, 2023 Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to consider shadows as background and therefore does not perform segmentation on them. In this paper, we propose ShadowSAM, a simple yet effective framework for fine-tuning SAM to detect shadows. Besides, by combining it with long
Yonghui Wang +3 more
openaire +3 more sources
Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination [PDF]
Sensors, 2020 A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult.
Manuel José Ibarra-Arenado +2 more
doaj +2 more sources
Learning Shadow Correspondence for Video Shadow Detection [PDF]
European Conference on Computer Vision, 2022 Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames, especially when the illumination and background textures change in a video.
Xinpeng Ding +3 more
openaire +3 more sources
Learning From Synthetic Shadows for Shadow Detection and Removal [PDF]
IEEE Transactions on Circuits and Systems for Video Technology, 2021 Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), v2: fixed ...
Naoto Inoue, Toshihiko Yamasaki
openaire +4 more sources
Image Shadow Detection and Removal Based on Region Matching of Intelligent Computing. [PDF]
Comput Intell Neurosci, 2022 Shadow detection and removal play an important role in the field of computer vision and pattern recognition. Shadow will cause some loss and interference to the information of moving objects, resulting in the performance degradation of subsequent ...
Feng J, Kim YK, Liu P.
europepmc +2 more sources
Cloud and Cloud Shadow Detection of GF-1 Images Based on the Swin-UNet Method
Atmosphere, 2023 Cloud and cloud shadow detection in remote sensing images is an important preprocessing technique for quantitative analysis and large-scale mapping. To solve the problems of cloud and cloud shadow detection based on Convolutional Neural Network models ...
Yuhao Tan +7 more
doaj +2 more sources
A novel single robot image shadow detection method based on convolutional block attention module and unsupervised learning network [PDF]
Frontiers in Neurorobotics, 2022 Shadow detection plays a very important role in image processing. Although many algorithms have been proposed in different environments, it is still a challenging task to detect shadows in natural scenes.
Jun Zhang, Junjun Liu
doaj +2 more sources
Fine-Context Shadow Detection using Shadow Removal [PDF]
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 Current shadow detection methods perform poorly when detecting shadow regions that are small, unclear or have blurry edges. In this work, we attempt to address this problem on two fronts. 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
Valanarasu, Jeya Maria Jose +1 more
openaire +3 more sources
ORTHOPHOTO SHADOW DETECTION METHOD UNDER ARTIFICIAL SHADOW [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Shadows are ubiquitous in high-resolution images, especially in urban regions where there are more serious shadow occlusions. In order to improve the detection effect of shadows, this paper analyzes the characteristics and properties of shadows in ...
W. X. Zhang +6 more
doaj +3 more sources
When SAM Meets Shadow Detection [PDF]
arXiv.org, 2023 As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc.
Jie, Leiping, Zhang, Hui
openaire +3 more sources