IMR OpenIR
Evaluation of shadow features
Qu, Liangqiong1,2,3; Tian, Jiandong1; Fan, Huijie1; Li, Wentao1; Tang, Yandong1
Corresponding AuthorTian, Jiandong(tianjd@sia.cn)
2018-02-01
Source PublicationIET COMPUTER VISION
ISSN1751-9632
Volume12Issue:1Pages:95-103
AbstractShadow features such as colour ratio, texture, and chromaticity have proved to be quite effective in shadow detection. Many shadow detection methods have been proposed on the basis of different features. However, previous works for shadow detection mainly focus on designing an effective classifier for existing shadow features, but pay less attention on the analysis of shadow features themselves. The majority of studies simply report the final shadow detection results rather than make an evaluation on each feature. Readers often do not know which features are more effective or whether these shadow features are complementary. The following problems are still unsolved: the robustness of each feature, which feature plays the most important role in a detection method, and what is the best performance that current features can reach. The purpose of this study is to answer these questions, and the authors hope that this study can offer guidance for future shadow detection algorithms via the evaluation of frequently used shadow features. Several useful and interesting conclusions are obtained after conducting extensive comparison experiments on a large dataset.
Keywordobject detection image texture image colour analysis image classification shadow features colour ratio chromaticity shadow detection methods
Funding OrganizationNatural Science Foundation of China ; Youth Innovation Promotion Association CAS
DOI10.1049/iet-cvi.2017.0159
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China[61473280] ; Natural Science Foundation of China[61333019] ; Natural Science Foundation of China[91648118] ; Youth Innovation Promotion Association CAS
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000423200200011
PublisherINST ENGINEERING TECHNOLOGY-IET
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/127222
Collection中国科学院金属研究所
Corresponding AuthorTian, Jiandong
Affiliation1.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang, Liaoning, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Qu, Liangqiong,Tian, Jiandong,Fan, Huijie,et al. Evaluation of shadow features[J]. IET COMPUTER VISION,2018,12(1):95-103.
APA Qu, Liangqiong,Tian, Jiandong,Fan, Huijie,Li, Wentao,&Tang, Yandong.(2018).Evaluation of shadow features.IET COMPUTER VISION,12(1),95-103.
MLA Qu, Liangqiong,et al."Evaluation of shadow features".IET COMPUTER VISION 12.1(2018):95-103.
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