Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter-Free Radial Distortion Correction with Center of Distortion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algebraic Approach to Lens Distortion by Line Rectification
Journal of Mathematical Imaging and Vision
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
WAMUS'07 Proceedings of the 7th WSEAS international conference on Wavelet analysis & multirate systems
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification.