RI | Publications | Dynamic Relevance: Vision-based focus of attention using artificial neural networks

Text only version of this site

Dynamic Relevance: Vision-based focus of attention using artificial neural networks
S. Baluja and D. Pomerleau
Artificial Intelligence 97, 1997, pp. 381-395.

Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference

Download [Help]

Adobe portable document format (pdf) [86 KB]
Compressed postscript (ps.gz) [79 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

This paper presents a method for ascertaining the relevance of inputs in vision-based tasks by exploiting temporal coherence and predictability. In contrast to the tasks explored in many previous relevance experiments, the class of tasks examined in this study is one in which relevance is a time-varying function of the previous and current inputs. The method proposed in this paper dynamically allocates relevance to inputs by using expectations of their future values. As a model of the task is learned, the model is simultaneously extended to create task-specific predictions of the future values of inputs. Inputs that are not relevant, and therefore not accounted for in the model, will not be predicted accurately. These inputs can be de-emphasized, and, in turn, a new, improved, model of the task created. The techniques presented in this paper have been successfully applied to the vision-based autonomous control of a land vehicle, vision-based hand tracking in cluttered scenes, and the detection of faults in the plasma-etch step of semiconductor wafers.

Notes

Associated center: VASC

Text Reference

S. Baluja and D. Pomerleau, "Dynamic Relevance: Vision-based focus of attention using artificial neural networks," Artificial Intelligence 97, 1997, pp. 381-395.

BibTeX Reference

@inproceedings{Baluja_1997_930,
   author = "Shumeet Baluja and Dean Pomerleau",
   title = "Dynamic Relevance: Vision-based focus of attention using artificial neural networks",
   booktitle = "Artificial Intelligence 97",
   year = "1997",
   pages = "381-395"
}