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| Content Provider | Springer Nature Link |
|---|---|
| Author | Rivlin, Ehud Rotstein, Héctor |
| Copyright Year | 2000 |
| Abstract | Several characteristics of the human oculomotor system have been suggested to be useful also for active vision mechanisms. Among others, foveal vision and a tracking scheme based on two different modes, called smooth pursuit and saccade have often been postulated or implemented. The purpose of this paper is to formulate a setup in which the benefit of implementing these schemes can be evaluated in a systematic manner, based on control considerations but incorporating image processing constraints. First, the advantage of using foveal vision is evaluated by computing the size of the foveal window which will allow tracking of the largest possible class of signals. By using linear optimal control theory, this problem can be formulated as a one-variable maximization.Second, foveal vision leads naturally to smooth pursuit, defined as the performance that can be achieved by the controller resulting in the optimal size of the foveal window. This controller is relatively simple (i.e., linear, time-invariant) as is to be expected for this control loop.Finally, when smooth pursuit fails a corrective action must be performed to re-center the target on the fovea. Recent results in linear optimal control, provide the necessary tools for addressing this challenging problem in a systematic manner. |
| Starting Page | 81 |
| Ending Page | 96 |
| Page Count | 16 |
| File Format | |
| ISSN | 09205691 |
| Journal | International Journal of Computer Vision |
| Volume Number | 39 |
| Issue Number | 2 |
| e-ISSN | 15731405 |
| Language | English |
| Publisher | Kluwer Academic Publishers |
| Publisher Date | 2000-01-01 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition Software |
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