000183773 001__ 183773
000183773 005__ 20190316235551.0
000183773 037__ $$aCONF
000183773 245__ $$aAn Optimal Policy for Target Localization with Application to Electron Microscopy
000183773 269__ $$a2013
000183773 260__ $$c2013
000183773 336__ $$aConference Papers
000183773 520__ $$aThis paper considers the task of finding a target location by making a limited number of sequential observations. Each observation results from evaluating an imperfect classifier of a chosen cost and accuracy on an interval of chosen length and position. Within a Bayesian framework, we study the problem of minimizing an objective that combines the entropy of the posterior distribution with the cost of the questions asked. In this problem, we show that the one-step lookahead policy is Bayes-optimal for any arbitrary time horizon. Moreover, this one-step lookahead policy is easy to compute and implement. We then use this policy in the context of localizing mitochondria in electron microscope images, and experimentally show that significant speed ups in acquisition can be gained, while maintaining near equal image quality at target locations, when compared to current policies.
000183773 6531_ $$aComputer Vision
000183773 6531_ $$aElectron Microscopy
000183773 6531_ $$aFast Imaging
000183773 6531_ $$aOptimal Control
000183773 6531_ $$aTarget Localization
000183773 700__ $$0245861$$aSznitman, Raphael$$g177109
000183773 700__ $$0242715$$aLucchi, Aurélien$$g185205
000183773 700__ $$aFrazier, Peter I.
000183773 700__ $$aJedynak, Bruno M.
000183773 700__ $$0240252$$aFua, Pascal$$g112366
000183773 7112_ $$aInternational Conference on Machine Learning (ICML)$$cAtlanta, GA, USA$$dJune 16-21, 2013
000183773 8564_ $$uhttps://sites.google.com/site/sznitr/research/optimal_policy_sem$$zURL
000183773 8564_ $$s2529829$$uhttps://infoscience.epfl.ch/record/183773/files/icml2013.pdf$$yPreprint$$zPreprint
000183773 8564_ $$s8826444$$uhttps://infoscience.epfl.ch/record/183773/files/video_optPolicy.avi$$yAssociated Video$$zAssociated Video
000183773 909C0 $$0252087$$pCVLAB$$xU10659
000183773 909CO $$ooai:infoscience.tind.io:183773$$pconf$$pIC$$qGLOBAL_SET
000183773 917Z8 $$x177109
000183773 917Z8 $$x112366
000183773 937__ $$aEPFL-CONF-183773
000183773 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000183773 980__ $$aCONF