Name
Abstract | ||
|---|---|---|
Recently, the key technique of image processing has been widely applied to pattern recognition, content retrieval, and object segmentation. These applications have brought much higher complexity in image computation. Accordingly, the processed results may be interfered due to the interlacing reference. To overcome this problem, researchers have developed the object detection mechanism, which is a preprocessing procedure to extract significant feature to stand for the whole image. However, the error rate of detection is a crucial challenge in this research field. Based on the concept of manifold ranking, we have designed a brand-new object detection method considering both local and global features. The experimental results have demonstrated that the new method is able to lower down the detection error rate in case that the object located near the boundary. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-019-08299-1 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Manifold ranking, Object detection, Salient map, Edge detecting prior | Object detection,Computer vision,Interlacing,Pattern recognition,Computer science,Segmentation,Word error rate,Image processing,Preprocessor,Artificial intelligence,Salient,Computation | Journal |
Volume | Issue | ISSN |
79 | 9 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yung-Chen Chou | 1 | 118 | 16.59 |
| Yu-Wei Nien | 2 | 0 | 0.34 |
| Ying-Chin Chen | 3 | 1 | 2.04 |
| Bo Li | 4 | 0 | 0.34 |
| Jung-San Lee | 5 | 353 | 30.52 |