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Figure 5 Distribution of features.
Figure 3 Non-uniform distribution of features.
The UBM characterizes the person-independent distribution of features of the subject population expected during recognition.
Figure 1 Distribution of features for the Harris corner detector in a color image.
Rauber et al. [8] used 'islands of music' to visualize music based on a distribution of features.
An example of the distribution of features used in present work is shown in Figure 6 for detections of strong (a) and weak (b) signals.
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Positive training data tend to have a well concentrated distribution of feature values while negative training data have a scattered distribution of feature values.
Second, positive training data have a well concentrated distribution of feature values while negative training data have a scattered distribution of feature values.
Figures 6 and 7 indicate the distribution of feature values of labeled points.
Analysis of distribution of feature values revealed that they contain significant inter-personal differences.
And the distribution of feature maps after convolution is naturally different from that without whitening processing.
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