Indonesian J Elec Eng & Comp Sci
ISSN: 2502-4752
3D face creation via 2D images within blender virtual environment (Ali Salim Rasheed)
461
When this calculation is made, the edge will be detected where is a spatial change in intensity pixel values is
strong rapidly changed.
In our approach, the Laplace filter used to construct a blurred image classifier to isolate blurred
images from non-blurred images by using a suitable threshold value, in which the value under the threshold is
selected it considered as blurred 2D image and up which it would be considered as a non-blurred image. This
can be calculated using a convolution filter. Since the input image is represented as a set of discrete pixels,
we have to find a discrete convolution kernel that can approximate the second derivatives in the definition of
the Laplacian. Two commonly used small kernels are shown in Figure 5.
Figure 5. Two commonly used discrete approximations to the Laplacian filter. (Note, we have defined the
Laplacian using a negative peak because this is more common; however, it is equally valid to use the
opposite sign convention)
Using one of these kernels, the Laplacian can be calculated using standard convolution methods.
Because these kernels are approximating a second derivative measurement on the image, they are
very sensitive to noise. To counter this, the image is often Gaussian smoothed before applying the Laplacian
filter. This pre-processing step reduces the high frequency noise components prior to the differentiation step.
In fact, since the convolution operation is associative, we can convolve the Gaussian smoothing
filter with the Laplacian filter first of all, and then convolve this hybrid filter with the image to achieve the
required result. Doing things this way has two advantages:
Since both the Gaussian and the Laplacian kernels are usually much smaller than the image, this
method usually requires far fewer arithmetic operations. The LoG (`Laplacian of Gaussian') kernel can be
precalculated in advance so only one convolution needs to be performed at run-time on the image.
The 2-D LoG function centered on zero and with Gaussian standard deviation
has the form:
