Instrumentation & Measurement Magazine 23-6 - 25

Fig. 4. SAR images with area of interest: (a) April 10, 1992 and (b) June 21, 1996.

6.	Calculate the Histogram and normalize it to the range
[0, 255]
7.	Display backproj
8.	Draw the 1-D histogram of the image
In these steps, "Hist_and_Backproj" is a function which
collects the values of the selected channels in the input images and finds the corresponding histogram bin; the function
"backproj" calculates the probability of each element value in
relationship with the empirical probability distribution represented by the histogram.
The results are displayed in Fig. 6a and Fig. 6b. Both figures depict the processing of SAR images with an overlapping
of the rectangular area of interest in L-band (left image), while
the right image illustrates the grey levels (abscise) with respect
to pixels (ordinates). For now, we can see a peak of grey scale

Inside Python-based Program
The calcBackProject functions calculate the back project of
the histogram. That is, similar to calcHist, at each location (x, y), the function collects the values of the selected
channels in the input images and finds the corresponding histogram bin. But instead of incrementing it, the
function reads the value of the bin, scales it by scale and
stores it in backProject (x, y). In terms of statistics, the function calculates the probability of each element value in
relation to the empirical probability distribution represented by the histogram. Details are illustrated in the
instructions:

Fig. 5. Proposed open computer vision-based algorithm for processing
features within images.
September 2020	

IEEE Instrumentation & Measurement Magazine	25



Instrumentation & Measurement Magazine 23-6

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