Iterative training of discriminative models for the generalized hough transform

Authors:
Heike Ruppertshofen;Cristian Lorenz;Sarah Schmidt;Peter Beyerlein;Zein Salah;Georg Rose;Hauke Schramm
Affiliations:
Institute of Applied Computer Science, University of Applied Sciences Kiel and Institute of Electronics, Signal Processing and Communication Technology, Otto-von-Guericke University Magdeburg, Ger ...;Department Digital Imaging, Philips Research Hamburg, Germany;Department of Engineering, University of Applied Sciences Wildau and Institute of Electronics, Signal Processing and Communication Technology, Otto-von-Guericke University Magdeburg, Germany;Department of Engineering, University of Applied Sciences Wildau, Germany;Institute of Electronics, Signal Processing and Communication Technology, Otto-von-Guericke University Magdeburg, Germany;Institute of Electronics, Signal Processing and Communication Technology, Otto-von-Guericke University Magdeburg, Germany;Institute of Applied Computer Science, University of Applied Sciences Kiel, Germany
Venue:
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Year:
2010

Citing 4
Cited 0

Robust Object Detection with Interleaved Categorization and Segmentation

International Journal of Computer Vision
Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images

IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Improving a discriminative approach to object recognition using image patches

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a discriminative approach to the Generalized Hough Transform (GHT) employing a novel fully-automated training procedure for the estimation of discriminative shape models. The technique aims at learning the shape and variability of the target object as well as further confusable structures (anti-shapes), visible in the training images. The integration of the learned target shape and anti-shapes into a single GHT model is implemented straightforwardly by positive and negative weights. These weights are learned by a discriminative training and utilized in the GHT voting procedure. In order to capture the shape and anti-shape information from a set of training images, the model is built from edge structures surrounding the correct and the most confusable locations. In an iterative procedure, the training set is gradually enhanced by images from the development set on which the localization failed. The proposed technique is shown to substantially improve the object localization capabilities on long-leg radiographs.