Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
A Stitching Algorithm for Automatic Registration of Digital Radiographs
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
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
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
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A fully automatic iterative training approach for the generation of discriminative shape models for usage in the Generalized Hough Transform (GHT) is presented. The method aims at capturing the shape variability of the target object contained in the training data as well as identifying confusable structures (anti-shapes) and integrating this information into one model. To distinguish shape and anti-shape points and to determine their importance, an individual positive or negative weight is estimated for each model point by means of a discriminative training technique. The model is built from edge points surrounding the target point and the most confusable structure as identified by the GHT. Through an iterative approach, the performance of the model is gradually improved by extending the training dataset with images, where the current model failed to localize the target point. The proposed method is successfully tested on a set of 670 long-leg radiographs, where it achieves a localization rate of 74---97% for the respective tasks.