Name
Abstract | ||
|---|---|---|
A biologically inspired foveated attention system in an object detection scenario is proposed. Thereby, a high-performance active multi-focal camera system imitates visual behaviors such as scan, saccade and fixation. Bottom-up attention uses wide-angle stereo data to select a sequence of fixation points in the peripheral field of view. Successive saccade and fixation of high foveal resolution using a telephoto camera enables high accurate object recognition. Once an object is recognized as target object, the bottom-up attention model is adapted to the current environment, using the top-down information extracted from this target object. The bottom-up attention model and the object recognition algorithm based on SIFT are implemented using CUDA technology on Graphics Processing Units (GPUs), which highly accelerates image processing. In the experimental evaluation, all the target objects were detected in different backgrounds. Evident improvements in accuracy, flexibility and efficiency are achieved. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ROBOT.2009.5152354 | ICRA |
Keywords | Field | DocType |
fixation point,bottom-up attention,foveated attention system,active multi-focal vision system,target object,high foveal resolution,object recognition algorithm,object detection scenario,high accurate object recognition,bottom-up attention model,high-performance active multi-focal camera,machine vision,sift,image processing,kalman filters,layout,top down,vision system,field of view,bottom up,image resolution,information extraction,object recognition,data mining | Object detection,Computer vision,Scale-invariant feature transform,Viola–Jones object detection framework,3D single-object recognition,Machine vision,Computer science,Image processing,Foveal,Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1050-4729 |
Citations | PageRank | References |
6 | 0.48 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingting Xu | 1 | 123 | 9.59 |
| Hao Wu | 2 | 6 | 0.48 |
| Tianguang Zhang | 3 | 74 | 7.37 |
| Kolja Kühnlenz | 4 | 430 | 40.87 |
| Martin Buss | 5 | 1799 | 159.02 |