Subspace Vector Quantization and Markov Modeling for Cell Phase Classification
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
An optimally weighted fuzzy k-NN algorithm
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
| Hi-index | 0.00 |
Pixel Position Regression (PPR), an automatic supervised method for image segmentation, is presented. The method uses a set of corresponding points indicated in each train image. For each point in this set, the mean position in all train images is determined. By warping the set of corresponding points to their mean positions, one can associate with each position in each train image a reference position. PPR estimates the reference position from a rich set of local image features through k-nearest-neighbor regression. The deformation field thus obtained determines the segmentation. It is demonstrated that the deformation field estimate can be improved by (weighted) blurring and more sophisticated methods such as global modeling of the deformation field through principal component analysis and iterated regression. The method is evaluated on a set of chest radio-graphs in which the lung fields, heart and clavicles are segmented.