4. Computing The Primary Area
In this research, the primary area of the desired image (burnt region, in figure 3) is
calculated after binarization and noise cancellation in two methods;
4.1 Computing of area in integration method
The availble burnt region in figure 10 is a binary image array consisting of values of
0s and 1s. In fact, this image has two colors of black (the burnt region) and white
(background). It is enough to count the number of black pixels, i.e. the number of 1
available in the image to obtain the primary area by integration method (i.e. the sum of
the pixels 1 which represents the approximate value of the area) [13].
4.2 Computing of area using of interconnected components extraction method
In this method, first using of some instructions in MATLAB language, we first select
the desired binary object, then we extract the pixels connected to it from the remaining
image to calculate the area of that object ultimately [14].
4. Computing The Primary Area
In this research, the primary area of the desired image (burnt region, in figure 3) is
calculated after binarization and noise cancellation in two methods;
4.1 Computing of area in integration method
The availble burnt region in figure 10 is a binary image array consisting of values of
0s and 1s. In fact, this image has two colors of black (the burnt region) and white
(background). It is enough to count the number of black pixels, i.e. the number of 1
available in the image to obtain the primary area by integration method (i.e. the sum of
the pixels 1 which represents the approximate value of the area) [13].
4.2 Computing of area using of interconnected components extraction method
In this method, first using of some instructions in MATLAB language, we first select
the desired binary object, then we extract the pixels connected to it from the remaining
image to calculate the area of that object ultimately [14].
การแปล กรุณารอสักครู่..