Iterative training of discriminative models for the generalized hough transform
作者： Heike Ruppertshofen , Cristian Lorenz , Sarah Schmidt , Peter Beyerlein , Zein Salah
DOI: 10.1007/978-3-642-18421-5_3
关键词:
摘要: We present a discriminative approach to the Generalized Hough Transform (GHT) employing novel fully-automated training procedure for estimation of shape models. The technique aims at learning and variability target object as well further confusable structures (anti-shapes), visible in images. integration learned anti-shapes into single GHT model is implemented straightforwardly by positive negative weights. These weights are utilized voting procedure. In order capture anti-shape information from set images, built edge surrounding correct most locations. an iterative procedure, gradually enhanced images development on which localization failed. proposed shown substantially improve capabilities long-leg radiographs.
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