IDEC DATAVS2 Series - Manual (Page 81)

Instruction Manual SVS2 Series
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16.8. ► Inspection times
Total inspection duration depends on three factors:
• exposure time (►);
• acquisition time;
• processing time
Exposure time: Time period during which the acquisition device is exposed to light. The longer the
exposure time, the greater the quantity of light entering the device. The duration of this time period is
determined by:
o
speed of parts to be inspected: high speeds require shorter exposure times or images will be
blurred;
o
inspection pace: this is a constraint on exposure time, which is supposed to be short to allow
a large number of objects to be inspected;
o
available light: the better the lighting, the fewer the acquisition problems due to low exposure
values.
Where reducing exposure time is a must, certain arrangements might help preserving the quality of
acquired images:
o
increasing the brightness of the inspection area;
o
increasing CMOS gain, where gain (output/input ratio) means an increase in brightness,
which, on the other hand, may worsen image quality;
o
using variable-aperture lenses: the greater the cavity that lets light pass past the lenses, the
lower the image depth (please note that focusing an object may prove difficult when object
distance from the lenses is variable).
Acquisition time: Time taken by the sensor to capture an image. After the CMOS has been exposed to
light/image during the exposure time, the image must be transferred to device memory. It takes about
30 ms to transfer a complete image. This time is significantly reduced if only a part of the total image is
acquired.
Processing time: Time taken to process the acquired image. It depends on the operations and tools
used for the inspection.
16.9. ► Contour match
In this analysis method, objects are recognized by comparing their contour. Binarized images are
analysed to identify those among the so-called BIS (Binary Image Set) that are made up of black
pixels only. The contour (which is not part of the BIS) is considered as a closed line along the border
of the binary image.
The contour match process is capable of identifying both internal and external contours.
To achieve recognition, first create a database of master images supplemented with certain data (such
as position, number of pixels, size) which will be used for the comparison.
During the inspection, the acquired image undergoes the same process and the results are compared
to those stored in the database.
Match score is determined and expressed as the difference between sample value and detected
target values: each element of the target image is matched to the closest database element.
The contour match method offers such benefits as an interesting processing speed and the ability to
handle objects at different orientations.
While providing greater accuracy than blob analysis, the contour match method has the same
drawback: less than ideal differentiation of object from the background, inability to recognise stacked
objects or objects touching each other.