The methodology is applied to face and interest point detection and to the RANSAC robust estimator. Error rates of the face detector proposed algorithm are comparable to the state-of-the-art methods. In the interest point application, the output of the Hessian-Laplace detector [Mikolajczyk-IJCV04] is approximated by a sequential WaldBoost classifier which is about five times faster than the original with comparable repeatability. A sequential strategy based on Wald's SPRT for evaluation of model quality in RANSAC leads to significant speed-up in geometric matching problems.
@InProceedings{MatasICRA2007,
author = {Matas, Ji{\vr}{\'i} and {\vS}ochman, Jan},
title = {Wald's Sequential Analysis for Time-constrained
Vision Problems},
year = {2007},
pages = {10},
booktitle = {IEEE International Conference on Robotics and Automation,
Workshops and Tutorials},
editor = {Seth Hutchinson},
isbn = {none},
month = {April},
day = {10-14},
venue = {Roma, Italy},
organization = {IEEE},
annote = {In detection and matching problems in computer vision,
both classification errors and time to decision characterize the
quality of an algorithmic solution. We show how to formalize such
problems in the framework of sequential decision-making and derive
quasi-optimal time-constrained solutions for three vision
problems.
The methodology is applied to face and interest point detection
and to the RANSAC robust estimator. Error rates of the face
detector proposed algorithm are comparable to the state-of-the-art
methods. In the interest point application, the output of the
Hessian-Laplace detector [Mikolajczyk-IJCV04] is approximated by a
sequential WaldBoost classifier which is about five times faster
than the original with comparable repeatability. A sequential
strategy based on Wald's SPRT for evaluation of model quality in
RANSAC leads to significant speed-up in geometric matching
problems.},
keywords = {sequential analysis, WaldBoost, RANSAC, interest points},
note = {CDROM, invited paper},
psurl = {[PDF, 2.5 MB] },
}