Name
Abstract | ||
|---|---|---|
An object detection method from line drawing images is presented. In this method, the content of line drawing images are hierarchically represented, where a local neighborhood structure is formed for each primitive by grouping its nearest neighbors. The detection process is a hypothesis verification scheme. Firstly, the top k most similar local structures in the object drawing are obtained for each local structure of the model, and the corresponding transformation parameters are estimated. By treating each estimation result as a point in the parameter space, a dense region around the ground truth is then formed provided that there exists a model in the object drawing. At last, the mode detection method is used to find this dense region, and the significant modes are accepted as the occurrence of object instances. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03767-2_112 | CAIP |
Keywords | Field | DocType |
similar local structure,detection process,corresponding transformation parameter,dense region,local structure,object instance,hierarchical matching,mode detection method,line drawings,local neighborhood structure,shape detection,object drawing,object detection method,ground truth,parameter space,nearest neighbor | Object detection,Computer vision,Existential quantification,Pattern recognition,Computer science,Mode (statistics),Local structure,Ground truth,Parameter space,Artificial intelligence,Hypothesis verification,Line drawings | Conference |
Volume | ISSN | Citations |
5702 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rujie Liu | 1 | 147 | 15.49 |
| Yuehong Wang | 2 | 72 | 4.66 |
| Takayuki Baba | 3 | 77 | 8.19 |
| Daiki Masumoto | 4 | 76 | 6.33 |