Results 271 to 280 of about 4,979,837 (328)

Adaptive Illumination Mapping for Shadow Detection in Raw Images

IEEE International Conference on Computer Vision, 2023
Shadow detection methods rely on multi-scale contrast, especially global contrast, information to locate shadows correctly. However, we observe that the camera image signal processor (ISP) tends to preserve more local contrast information by sacrificing ...
Jiayu Sun, Ke Xu, Youwei Pang, Lihe Zhang, Huchuan Lu, G. Hancke, Rynson W. H. Lau   +6 more
semanticscholar   +1 more source

RMLANet: Random Multi-Level Attention Network for Shadow Detection and Removal

IEEE transactions on circuits and systems for video technology (Print), 2023
This paper addresses the problem of shadow detection and shadow removal from a single image. Despite awareness of utilizing both local and global contexts, previous works only aggregate features level by level in a coarse-to-fine manner. To overcome this
Lei Jie, Hui Zhang
semanticscholar   +1 more source

Mitigating Intensity Bias in Shadow Detection via Feature Decomposition and Reweighting

IEEE International Conference on Computer Vision, 2021
Although CNNs have achieved remarkable progress on the shadow detection task, they tend to make mistakes in dark non-shadow regions and relatively bright shadow regions. They are also susceptible to brightness change.
Lei Zhu, Ke Xu, Zhanghan Ke, Rynson W. H. Lau   +3 more
semanticscholar   +1 more source

Exploring better target for shadow detection

Knowledge-Based Systems, 2023
Shadow detection aims to identify shadow regions from images, which plays a significant role in scene understanding. Existing approaches tend to ignore the annotation noises in ground truths, which will be overfitted in the later training phase and ...
Wen Wu, Xiao-Diao Chen, Wenyang Yang, J. Yong   +3 more
semanticscholar   +1 more source

Single Image Shadow Detection via Complementary Mechanism

ACM Multimedia, 2022
In this paper, we present a novel shadow detection framework by investigating the mutual complementary mechanisms contained in this specific task.
Yurui Zhu, Xueyang Fu, Chengzhi Cao, Xi Wang, Qibin Sun, Zhengjun Zha   +5 more
semanticscholar   +1 more source

Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects

Isprs Journal of Photogrammetry and Remote Sensing, 2022
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Zhiwei Li, Huanfeng Shen, Qihao Weng, Yuzhuo Zhang, Peng Dou, Liangpei Zhang   +5 more
semanticscholar   +1 more source

Shadow Detection Using DenseUNet

2021
The objective of this paper is shadow detection. We propose a novel semantic segmentation approach based on DenseUNet. The UNet is an encoder–decoder architecture consisting of an encoder and a decoder. The encoder is used for downsampling the image and the decoder for upsampling the image to produce the required output.
Satyajeet Singh, Sandeep Yadav, Antoreep Jana, Seba Susan   +3 more
openaire   +1 more source

A Shadow Detection Algorithm Based on Multiscale Spatial Attention Mechanism for Aerial Remote Sensing Images

IEEE Geoscience and Remote Sensing Letters, 2022
Automated shadow detection is an important research problem in the field of remote sensing image processing. The shadow regions seriously affect the interpretation of the remote sensing images. However, the existing methods have poor detection effect for
Dongyang Liu, Junping Zhang, Yinhu Wu, Yehui Zhang   +3 more
semanticscholar   +1 more source

Shadow Detecting and Shadow Interpolation Algorithm for InSAS

Advanced Materials Research, 2012
The shadow detecting algorithm based on the coherence and the Sigma filter is used to pick up the shadow of interferometric synthetic aperture sonar (InSAS), which can eliminate small separated shadow areas. To solve the problems such as great computer complexity of traditional Shepard interpolation method and large fluctuant of linear interpolation ...
Sen Zhang, Ming Chen, Jin Song Tang
openaire   +1 more source

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

Computer Vision and Pattern Recognition, 2020
Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations.
Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, P. Heng   +5 more
semanticscholar   +1 more source