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Following the feature extraction phases, extracted feature histograms are mapped on spatial information preserving multilevel and multi-resolution bag of features representation method named spatial pyramid matching.
Section 6 presents the experimental results to validate the performance of the algorithm in feature extraction and demonstrates the usage of extracted feature in target recognition and classification.
The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei.
Once the extracted feature is obtained, a support vector machine detects the traffic sign.
Then the extracted feature information is denoised by two-dimensional (2D) wavelet.
The extracted feature set is then given into the network for the classification.
Next, the extracted feature vectors are projected to a low-dimensional subspace using DLDA technique.
In addition, the extracted feature is also less affected by the underlying geologies than traditional Power Spectrum Density based features.
PCA is adopted to reduce the dimensionality of the extracted feature vectors, which leads to fast computation of the algorithm.
The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization.
Early gear failure is diagnosed exactly by demodulation of the extracted feature component, which is difficult to identify using the traditional method.
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CEO of Professional Science Editing for Scientists @ prosciediting.com