Wald's Sequential Analysis for Time-constrained Vision Problems

  • Jiří Matas
  • Jan Šochman
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 8)

In many decision problems in computer vision, both classification errors and time to decision characterise the quality of an algorithmic solution. This is especially true for applications of vision to robotics where real-time response is typically required.

Time-constrained classification, detection and matching problems can be often formalised in the framework of sequential decision-making. We show how to derive quasi-optimal time-constrained solutions for three different vision problems by applying Wald’s sequential analysis. In particular, we adapt and generalise Wald’s sequential probability ratio test (SPRT) and apply it to the three vision problems: (i) face detection, (ii) real-time detection of distinguished regions (interest points) and (iii) establishing correspondences by the RANSAC algorithm with application e.g. in SLAM, 3D reconstruction and object recognition.

In the face detection problem, we are interested in learning the fastest detector satisfying constraints on false positive and false negative rates.We solve the problem by WaldBoost [15], a combination of Wald’s sequential probability ratio test and AdaBoost learning [2]. The solution can be viewed as a principled way to build a close-to-optimal “cascade of classifiers” [22]. Naturally, the approach is applicable to other classes of objects.


Motion Estimation False Negative Rate Interest Point Face Detection Epipolar Geometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jiří Matas
    • 1
  • Jan Šochman
    • 1
  1. 1.Department of CyberneticsCzech Technical University in PragueCzech Republic

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