Up: Automatic extraction and classification
The normalized spectra were standardized and a metric was introduced in vector space
(Murtagh & Heck [1984]; Vieira & Ponz [1995]). After standardization, we
obtain

(3) 
with
being the mean value (over j variable) and
the standard deviation of
the
spectrum;
i=1,..., 426. The spectra have zero mean and unit standard deviation.
The metric is the standard unweighted Euclidean distance between two realvalued vectors
d_{ik} given by

(4) 
The minimum distance d_{ik} for the
spectrum for the class k was calculated with
displacement
pixels to predict a possible displacement from the detection algorithm caused by
the local background. The final result was given by



(5) 

Figure 4:
A characteristic
OB spectrum of our sample 

Figure 5:
A characteristic
A spectrum of our sample 

Figure 6:
A characteristic
F spectrum of our sample 

Figure 7:
A characteristic
G spectrum of our sample 

Figure 8:
A characteristic
K spectrum of our sample 

Figure 9:
A characteristic
M spectrum of our sample 
Up: Automatic extraction and classification
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