Name
Abstract | ||
|---|---|---|
Detecting and identifying Regions of Interest (ROIs) is an important task for navigation and retrieval services. In this paper, we focus on indoor scene images and detect object regions such as shop signs and merchandise. Our method is based on two approaches; 1) Indoor structure analysis from a single image by learning the types of scenes. 2) Detect ROIs by taking advantage of the relationship of expected locations of planes and objects. We conduct a detection experiment and demonstrate the effectiveness of our proposal. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | region of interest detection,region of interest identification,indoor scene images,learning (artificial intelligence),scene learning,navigation services,saliency map,retrieval services,roi detection,computerised navigation,image retrieval,object region detection,object detection,object recognition,natural scenes,indoor environment,indoor structure analysis,learning artificial intelligence |
Field | DocType | ISSN |
Structure analysis,Object detection,Computer vision,Viola–Jones object detection framework,Saliency map,Object-class detection,Pattern recognition,Computer science,Image retrieval,Artificial intelligence,Region of interest,Cognitive neuroscience of visual object recognition | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 1 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaori Kataoka | 1 | 9 | 1.68 |
| Kyoko Sudo | 2 | 52 | 8.42 |
| Masashi Morimoto | 3 | 17 | 4.71 |