TY - GEN
T1 - Stereo under sequential optimal sampling
T2 - 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
AU - Wang, Yilin
AU - Wang, Ke
AU - Dunn, Enrique
AU - Frahm, Jan Michael
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/24
Y1 - 2014/9/24
N2 - We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model. We operate over local image neighborhoods by iteratively estimating single pixel disparity values until sufficient evidence has been gathered to either validate or contradict the current hypothesis regarding local scene structure. The output of our sampling is a set of sampled pixel positions along with a robust and compact estimate of the set of disparities contained within a given region. We further propose an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set. Our sampling framework is a general pre-processing mechanism aimed at reducing computational complexity of disparity search algorithms by ascertaining a reduced set of disparity hypotheses for each pixel. Experiments demonstrate the effectiveness of the proposed approach when compared to state of the art methods.
AB - We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model. We operate over local image neighborhoods by iteratively estimating single pixel disparity values until sufficient evidence has been gathered to either validate or contradict the current hypothesis regarding local scene structure. The output of our sampling is a set of sampled pixel positions along with a robust and compact estimate of the set of disparities contained within a given region. We further propose an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set. Our sampling framework is a general pre-processing mechanism aimed at reducing computational complexity of disparity search algorithms by ascertaining a reduced set of disparity hypotheses for each pixel. Experiments demonstrate the effectiveness of the proposed approach when compared to state of the art methods.
UR - http://www.scopus.com/inward/record.url?scp=84911409289&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911409289&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2014.69
DO - 10.1109/CVPR.2014.69
M3 - Conference contribution
AN - SCOPUS:84911409289
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 485
EP - 492
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Y2 - 23 June 2014 through 28 June 2014
ER -