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IEEE Transactions on Geoscience and Remote Sensing, 2022
Shadow detection automatically marks shadow pixels in high-spatial-resolution (HSR) imagery with specific categories based on meaningful colorific features. Accurate shadow mapping is crucial in interpreting images and recovering radiometric information.
Qiqi Zhu +3 more
semanticscholar +1 more source
Shadow detection automatically marks shadow pixels in high-spatial-resolution (HSR) imagery with specific categories based on meaningful colorific features. Accurate shadow mapping is crucial in interpreting images and recovering radiometric information.
Qiqi Zhu +3 more
semanticscholar +1 more source
Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery
Remote Sensing of Environment, 2019We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 images. Three major innovative improvements were made as follows: (1) integration of auxiliary data, where Global ...
S. Qiu, Zhe Zhu, B. He
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Semi-supervised Video Shadow Detection via Image-assisted Pseudo-label Generation
ACM Multimedia, 2022Although learning-based methods have shown their potential for image shadow detection, video shadow detection is still a challenging problem. It is due to the absence of large-scale, temporally consistent annotated video shadow detection dataset. To this
Zipei Chen +3 more
semanticscholar +1 more source
Test-Time Intensity Consistency Adaptation for Shadow Detection
International Conference on Neural Information ProcessingShadow detection is crucial for accurate scene understanding in computer vision, yet it is challenged by the diverse appearances of shadows caused by variations in illumination, object geometry, and scene context.
Leyi Zhu +6 more
semanticscholar +1 more source
Shadow detection via multi-scale feature fusion and unsupervised domain adaptation
Journal of Visual Communication and Image Representation, 2022Shadow detection is significant for scene understanding. As a common scenario, soft shadows have more ambiguous boundaries than hard shadows. However, they are rarely present in the available benchmarks since annotating for them is time-consuming and ...
Kai Zhou +5 more
semanticscholar +1 more source
Timeline and Boundary Guided Diffusion Network for Video Shadow Detection
ACM MultimediaVideo Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD problem by considering the characteristic (i.e., boundary) of shadow ...
Haipeng Zhou +7 more
semanticscholar +1 more source
Single-Stage Instance Shadow Detection with Bidirectional Relation Learning
Computer Vision and Pattern Recognition, 2021Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region ...
Tianyu Wang +3 more
semanticscholar +1 more source
Tricolor Attenuation Model for Shadow Detection
IEEE Transactions on Image Processing, 2009Shadows, the common phenomena in most outdoor scenes, bring many problems in image processing and computer vision. In this paper, we present a novel method focusing on extracting shadows from a single outdoor image. The proposed tricolor attenuation model (TAM) that describe the attenuation relationship between shadow and its nonshadow background is ...
Jiandong, Tian, Jing, Sun, Yandong, Tang
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Spectral anomaly detection in deep shadows
Applied Optics, 2010Although several hyperspectral anomaly detection algorithms have proven useful when illumination conditions provide for enough light, many of these same detection algorithms fail to perform well when shadows are also present. To date, no general approach to the problem has been demonstrated.
Andrey V, Kanaev, Jeremy, Murray-Krezan
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Detecting shadows from a single image
Optics Letters, 2011We present a novel (to our best knowledge) optical recognition technique for detecting shadows from a single image. Most prior approaches definitely depend on explicit physical computational models, but physics-based approaches have the critical problem that they may fail severely even with slight perturbations.
Jung, C Jung, Chanho +2 more
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