| Evaluation of shadow features | |
| Qu, Liangqiong1,2,3; Tian, Jiandong1; Fan, Huijie1; Li, Wentao1; Tang, Yandong1 | |
| Corresponding Author | Tian, Jiandong(tianjd@sia.cn) |
| 2018-02-01 | |
| Source Publication | IET COMPUTER VISION
 |
| ISSN | 1751-9632 |
| Volume | 12Issue:1Pages:95-103 |
| Abstract | Shadow 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. |
| Keyword | object detection image texture image colour analysis image classification shadow features colour ratio chromaticity shadow detection methods |
| Funding Organization | Natural Science Foundation of China ; Youth Innovation Promotion Association CAS |
| DOI | 10.1049/iet-cvi.2017.0159 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Natural Science Foundation of China[61473280] ; Natural Science Foundation of China[61333019] ; Natural Science Foundation of China[91648118] ; Youth Innovation Promotion Association CAS |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000423200200011 |
| Publisher | INST ENGINEERING TECHNOLOGY-IET |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/127222 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Tian, Jiandong |
| Affiliation | 1.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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