DocumentCode
2795516
Title
A non-myopic approach to visual search
Author
Vogel, Julia ; Murphy, Kevin
Author_Institution
Univ. of British Columbia, Vancouver
fYear
2007
fDate
28-30 May 2007
Firstpage
227
Lastpage
234
Abstract
We show how a greedy approach to visual search - i.e., directly moving to the most likely location of the target - can be suboptimal, if the target object is hard to detect. Instead it is more efficient and leads to higher detection accuracy to first look for other related objects, that are easier to detect. These provide contextual priors for the target that make it easier to find. We demonstrate this in simulation using POMDP models, focussing on two special cases: where the target object is contained within the related object, and where the target object is spatially adjacent to the related object.
Keywords
Markov processes; control engineering computing; greedy algorithms; object detection; robot vision; greedy approach; nonmyopic approach; partially observed Markov decision process; robot; target object detection; visual search; Buildings; Computational modeling; Computer displays; Computer science; Computer vision; Detectors; Object detection; Robot sensing systems; Robot vision systems; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on
Conference_Location
Montreal, Que.
Print_ISBN
0-7695-2786-8
Type
conf
DOI
10.1109/CRV.2007.5
Filename
4228543
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