Sentence examples for classification of distortion from inspiring English sources
This method can be divided into two stages: classification of distortion type and evaluation utilizing distortion-specific methods based on the most suitable color channel.
The relationships of these two tests allows for the classification of distortion as gametic or zygotic [ 39].
For example, in cases where there was only one source of distortion, classification improvements of up to 8%and10%0% were achieved under babble noise and AWGN, respectively, using the SCF feature.
If we only use patches close to the CoD during the classification process, the effect of distortion correction with respect to feature sizes together with its potential benefits for texture analysis would be negligible, only artifacts caused by interpolation might degrade the classification result.
In [9] the impact of distortion correction on the classification accuracy regarding celiac disease images has been investigated.
The segmentation and blob generation stages absorb this type of distortion and makes that the detection and classification results are both excellent.
In this work the authors showed that only a few feature extraction methods are able to take a slight advantage of distortion correction (with respect to the overall classification rates).
In case only patches equidistant to the CoD would be used for classification, an analysis regarding the CoD distances and the according levels of distortion of the patches would be meaningless.
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.
The classification of these attributes to temporally localized distortions was based on earlier work [4], where it was shown that these attributes contribute almost of the total variance in the subjective scores (as shown in Figure 1).
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.
