Shading-aware shadow detection and removal from a single image

Fan, Xinyun; Wu, Wenjun; Zhang, Ling; Yan, Qingan; Fu, Gang; Chen, Zipei; Long, Chengjiang; Xiao, Chunxia

doi:10.1007/s00371-020-01916-3

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…For shadow generation, it is the result of blocked light, when the light from a light source is not able to reach the surface of an object due to the blockage of other objects, then the shadow is generated. [5] In motion target detection, some moving objects produce partial shadows due to the light, and as the target itself moves, the shadows are also in the same motion,so the ViBe algorithm will detect the motion of shadows as motion targets. In most of our scenes, the result of shadow motion is not what we expect, we only need the real motion target.…”

Section: Fusion Ghost Shadow Removal and Shadow Detection 41 Image Fu...mentioning

confidence: 99%

ViBe algorithm based on background fusion and channel calculation

He,

Zhou,

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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ViBe (Visual background extractor) algorithm is a motion target detection algorithm by background modeling, uses domain pixels for background modeling and updates the background model in real time. However, the traditional ViBe algorithm will produce disadvantages such as ghosting and shadow part false detection. This paper solves the problem of ghosting by image fusion mechanism, through the characteristics of the shadow itself, using the shadow part of the three-channel variance value for shadow detection and eliminate the shadow. The experiments show that our algorithm can quickly and effectively solve the ghost and shadow problems in the ViBe algorithm.

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Section: Fusion Ghost Shadow Removal and Shadow Detection 41 Image Fu...mentioning

confidence: 99%

ViBe algorithm based on background fusion and channel calculation

He,

Zhou,

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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“…The experiment platform is: Python programming language, TensorFlow package, Ubuntu 18.04, 16 GB memory, Inter i7 The proposed method is compared with three new shadow detection methods: GSCA-UNet (Jin et al, 2020), SAS (Fan et al, 2020), DSAN (Li et al, 2020). GSCA-UNet aimed to generate additional shadow images to enhance the generalization ability of the model.…”

Section: Experiments and Analysismentioning

confidence: 99%

A novel single robot image shadow detection method based on convolutional block attention module and unsupervised learning network

Zhang

Liu

2022

Front. Neurorobot.

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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. In this paper, we propose a convolutional block attention module (CBAM) and unsupervised domain adaptation adversarial learning network for single image shadow detection. The new method mainly contains three steps. Firstly, in order to reduce the data deviation between the domains, the hierarchical domain adaptation strategy is adopted to calibrate the feature distribution from low level to high level between the source domain and the target domain. Secondly, in order to enhance the soft shadow detection ability of the model, the boundary adversarial branch is proposed to obtain structured shadow boundary. Meanwhile, a CBAM is added in the model to reduce the correlation between different semantic information. Thirdly, the entropy adversarial branch is combined to further suppress the high uncertainty at the boundary of the prediction results, and it obtains the smooth and accurate shadow boundary. Finally, we conduct abundant experiments on public datasets, the RMSE has the lowest values with 9.6 and BER with 6.6 on ISTD dataset, the results show that the proposed shadow detection method has better edge structure compared with the existing deep learning detection methods.

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“…Hand‐crafted shadow removal . Early shadow removal methods are mostly based on physical models and the prior information, such as illumination [SL08, ZZX15a, FWZ*20, ZZX15b], gradient [FHLD05,FDL09] and color transfer [VHS17,WTBS07]. Some other methods adopt user interaction for shadow removal tasks.…”

Section: Related Workmentioning

confidence: 99%

Depth‐Aware Shadow Removal

Gai

Zhao

et al. 2022

Computer Graphics Forum

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Shadow removal from a single image is an ill‐posed problem because shadow generation is affected by the complex interactions of geometry, albedo, and illumination. Most recent deep learning‐based methods try to directly estimate the mapping between the non‐shadow and shadow image pairs to predict the shadow‐free image. However, they are not very effective for shadow images with complex shadows or messy backgrounds. In this paper, we propose a novel end‐to‐end depth‐aware shadow removal method without using depth images, which estimates depth information from RGB images and leverages the depth feature as guidance to enhance shadow removal and refinement. The proposed framework consists of three components, including depth prediction, shadow removal, and boundary refinement. First, the depth prediction module is used to predict the corresponding depth map of the input shadow image. Then, we propose a new generative adversarial network (GAN) method integrated with depth information to remove shadows in the RGB image. Finally, we propose an effective boundary refinement framework to alleviate the artifact around boundaries after shadow removal by depth cues. We conduct experiments on several public datasets and real‐world shadow images. The experimental results demonstrate the efficiency of the proposed method and superior performance against state‐of‐the‐art methods.

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Shading-aware shadow detection and removal from a single image

Cited by 15 publications

References 45 publications

ViBe algorithm based on background fusion and channel calculation

ViBe algorithm based on background fusion and channel calculation

A novel single robot image shadow detection method based on convolutional block attention module and unsupervised learning network

Depth‐Aware Shadow Removal

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