A Shape Consistency Measure for Improving the Generalized Hough Transform - Modified Voting Procedure for Discriminative Generalized Hough Transform based on Random Forest Confidence Measure.
Ferdinand Hahmann, Gordon Böer, Eric Gabriel, Carsten Meyer, Hauke Schramm · International Conference on Computer Vision Theory and Applications · 2015
The Discriminative Generalized Hough Transform (DGHT) is a general object localization approach. Based on a training corpus with annotated target point locations it employs a discriminative training technique to generate weighted shape models for usage in a standard GHT voting procedure. The method has shown to successfully cover medium target object variability by aggregating model points, representing the different variants, in a single model. However, due to the independent treatment of model points in the GHT voting, mutually exclusive variations may support the same localization hypothesis, leading to false positives. The problem is addressed by analyzing the spatial pattern of model points, voting for a specific Hough cell, and learning the structural differences between successful and unsuccessful localizations. Random Forests are utilized to rate the regularity of model point patterns to provide the probability of a “regular shape”, indicating a successful localization. The approach is evaluated on a public corpus containing 3830 portrait images with strong head pose variation with a localization success rate of 99.2% for the iris of both eyes. This is an improvement of 2% compared to the DGHT baseline system which demonstrates the potential of the novel method to eliminatve an important source of mislocalizations.