Omron F250-UME Software Manual page 101

Switching Cameras
2-2-3 Filtering
CHECK
The image read by the Camera can be manipulated to create an image that is
easier to measure by using the 9 filtering methods: weak smoothing, strong
smoothing, dilation, erosion, median, edge enhancement, vertical edge extrac-
tion, horizontal edge extraction, and edge extraction. The filtering can be set
separately for Image 0 and Image 1.
The cursor can be moved on the image to see the effects of filtering. Select fil-
tering while confirming the results on the image.
Filtering
OFF ---
Weak Measurement objects
smoothing with slight surface irregu-
larity
Strong
smoothing
Dilation White measurement
objects with black noise
Erosion Black measurement
objects with white noise
Median Measurement objects
with slight surface irregu-
larity
Edge Blurred measurement
enhancement objects (e.g., due to light-
ing changes)
Vertical edge Measurement objects dif-
extraction ficult to extract due to bad
image contrast
Horizontal Measurement objects dif-
edge extrac- ficult to extract due to bad
tion image contrast
Edge extrac- Measurement objects dif-
tion ficult to extract due to bad
image contrast
Filter Size
The strength of the filtering can be selected if Frame/Field is set to Frame.
3 × 3 5 × 5
Weak Strong
+ The image changes each time these keys are pressed.
ENT
SHIFT
Target
---
Reduces the irregularity
through smoothing.
Select either weak or
strong smoothing.
Increases white to elimi-
nate black noise.
Reduces white to elimi-
nate the white noise.
Reduces surface irregu-
larities while maintaining
the contour.
Enhances edges between
bright and dark regions.
Extracts the vertical
edges (light and dark) of
an image.
Extracts the horizontal
edges (light and dark) of
an image.
Extracts the vertical
edges (light and dark) of
an image.
Section 2-2
Function Example
application
---
Stable
searches
Removal of
noise from
measurement
objects
Removal of
noise from
measurement
objects
Edge position-
ing (with no
reduction in
accuracy)
Edge position-
ing
Defect inspec-
tion (using
binary pro-
cessing)
Defect inspec-
tion (using
binary pro-
cessing)
Defect inspec-
tion (using
binary pro-
cessing)
2-2-(4)