Cast shadow detection based on the YCbCr color space and topological cuts

Shao, Quan; Xu, Chenchen; Zhou, Yu; Dong, Hongji

doi:10.1007/s11227-018-2558-4

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…However, the drawback of this approach was that dark areas were misclassified as shadow areas. In order to reduce the shadow misclassification phenomena, Xu et al [30] used normalized RGB (L2 norm) and a 1D-invariant image to generate shadow masks, while, Shao et al [31] proposed a color space-YCbCr and topological cutting for shadow detection.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Hussain

et al. 2020

Applied Sciences

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In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Hussain

et al. 2020

Applied Sciences

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“…Such information is difficult to obtain in the research process, which greatly limits the application scope of this method [19][20][21][22]. At present, feature-based methods are mainly used to study the color features of the image, and shadow extraction is carried out by using the color characteristics of the pixel gray scale of the shadow area in RGB, HSV, YCbCr, and other color spaces [23,24]. Tasi et al [25] calculate the global threshold of ratio image by the ratio of hue-equivalent components to intensity-equivalent components.…”

Section: Introductionmentioning

confidence: 99%

Shadow Removal from UAV Images Based on Color and Texture Equalization Compensation of Local Homogeneous Regions

et al. 2022

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Due to imaging and lighting directions, shadows are inevitably formed in unmanned aerial vehicle (UAV) images. This causes shadowed regions with missed and occluded information, such as color and texture details. Shadow detection and compensation from remote sensing images is essential for recovering the missed information contained in these images. Current methods are mainly aimed at processing shadows with simple scenes. For UAV remote sensing images with a complex background and multiple shadows, problems inevitably occur, such as color distortion or texture information loss in the shadow compensation result. In this paper, we propose a novel shadow removal algorithm from UAV remote sensing images based on color and texture equalization compensation of local homogeneous regions. Firstly, the UAV imagery is split into blocks by selecting the size of the sliding window. The shadow was enhanced by a new shadow detection index (SDI) and threshold segmentation was applied to obtain the shadow mask. Then, the homogeneous regions are extracted with LiDAR intensity and elevation information. Finally, the information of the non-shadow objects of the homogeneous regions is used to restore the missed information in the shadow objects of the regions. The results revealed that the average overall accuracy of shadow detection is 98.23% and the average F1 score is 95.84%. The average color difference is 1.891, the average shadow standard deviation index is 15.419, and the average gradient similarity is 0.726. The results have shown that the proposed method performs well in both subjective and objective evaluations.

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Processing Technology of Thematic Identification and Classification of Objects in the Multispectral Remote Sensing Imagery

Hnatushenko

Shedlovska²,

Shedlovsky³

2022

Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making

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Cast shadow detection based on the YCbCr color space and topological cuts

Cited by 7 publications

References 20 publications

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Shadow Removal from UAV Images Based on Color and Texture Equalization Compensation of Local Homogeneous Regions

Processing Technology of Thematic Identification and Classification of Objects in the Multispectral Remote Sensing Imagery

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