Name
Abstract | ||
|---|---|---|
This paper presents an effective framework for removing moving cast shadows. Taking the reflection property of object surface for shadow regions under static and fixed scenes, an approximation estimation strategy of bidirectional reflectance distribution function as illumination invariant feature is proposed. It is valid for different types of shadow scenes. In this paper, we propose a new multiple ratios-based technique to justify shadow type for each frame: intensity ratio, area ratio and edge ratio of shadow regions are introduced. According to shadow types, several specified strategies are designed. For weak shadows, multiple features fusion strategy is employed, including color constancy, texture consistency and illumination invariant. For strong shadows, illumination invariant is utilized to detect the umbra and color constancy is utilized to detect the penumbra. Moreover, a suite of shadow direction features is firstly proposed to identify penumbra. The proposed approach is verified in fourteen video sequences varying from weak to strong shadows. The experimental results demonstrate the effectiveness and robustness of the proposed method for both indoor and outdoor scenes compared with some state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMM.2019.2954752 | IEEE Transactions on Multimedia |
Keywords | DocType | Volume |
Moving shadows segmentation,bidirectional reflectance distribution function,illumination invariant,shadow direction features | Journal | 22 |
Issue | ISSN | Citations |
9 | 1520-9210 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baobing Wang | 1 | 58 | 12.69 |
| Zhao Yong | 2 | 90 | 14.85 |
| C. L. Philip Chen | 3 | 4022 | 244.76 |