image Comparison tool       

The Image Comparison Tool allows you to compare the current image to a specific template image. Each "Template" is correlated against the trained feature. The correlation score returns the readability, i.e. a percentage where 100% represents a perfectly readable feature. Template matching looks for any print defects,  the total defect area, and the largest defect area.

Methodology Template matching is the process of comparing a trained image to the current sample image and analyzing the error produced. During the train, the mean, variation, and threshold images are produced. During the Run, the error image is produced.

Left-clicking the Image Comparison button will run the tool according to the current settings.

Right-clicking on the Image Comparison button will display the edit menu:

Selecting Edit will bring up edit boxes that will allow you to make various adjustments.  Select the blue arrow on the upper right to scroll through the various selections. The blue arrow in the upper left will bring you back to the previous selection.


The Template page allows control of the parameters affecting the training of the template image. When training the template, we are creating the threshold image. The threshold image is comprised of the variation image. Where: Threshold = Offset (0-255) + Variation Constant /10 * Variation Image

Train Settings--

Offset (Threshold): The maximum constant noise allowed in judging a character in greylevels (0-255)

Variation: Constant by which to multiply the variation image. The constant is divided by 10.

Method: The Variation Image can be calculated by one of 3 methods
Variation- The Variation Image is the difference between the brightest pixels and the darkest pixels. This method requires statistical training where, in the set of images captured, the greatest variation is sorted.
Thin Edge- Uses edge detection to determine the edge pixels. These edge pixels form the variation image; thus, the edges are assumed to be the areas with the greatest tolerance for print defects.
Thick Edge- Uses thick edge detection to determine the "variation" image. This is like the thin edge method except with a greater tolerance for print defect on the edges. NOTE: Edge Variation Image does not require multiple images for the training set. A single image is used to calculate the variation image.

Run Settings--
When running the template, we are creating the error image. The error image is calculated as: Error = Absolute(sample – mean) - Threshold

Normalization: The process by which the current sample image brightness is adjusted to the trained image brightness. Hence, this allows compensation for differing light levels and for the setting.
Off- No normalization is carried out
On (Destructive)- The current sample image is adjusted so that its intensities match the trained sample. The current sample image pixel data is modified.
On (Non Destructive)- The current sample image is copied before it is adjusted. All further analysis is carried out on the copied modified sample image.

Minimum Defect Level (Noise): Defects smaller than this defined size (in pixels) are ignored.

Image Display--
As a utility to the operator this shows the different images in the trained template. Displayed can be the
Mean-The mean image
Minimum- The darkest pixels in the training set
Maximum- The brightest pixel in the training set
Variation- The Variation Image
Threshold- The threshold Image
Error- The Calculated Error Image


The Correlation page allows control of the comparison.

Location method: Used to define the search area.
Off- Search area if whole image.
At Trained Position-Uses the trained ROI area.
At Current ROI Position- Uses the current ROI as the search area.

Pad Size X/Pad Size Y: The added "pad size" to the trained ROI in pixels that defines the ROI used to locate the feature in question

Sub Sample X/Sub Sample Y: The correlation does a low pass search for the model. This low pass search is done before a high pass search is done. The low pass search speeds up the execution time to find the model.

Retrain: Retrains the Low Pass model only. The Train button programs the low pass and high pass model. This is useful when adjusting the Sub Sample X and Y factors and when the image under the current ROI is not used. Hence, retraining is independent on the current ROI position.

Image Display: Displays the selected image.
Model- Show image of feature being sorted.
Model Low Resolution- Shows how the model looks in low resolution mode.
Result Image- Shows the results of the correlation. This shows while spots.


The Parameters page shows the results of the tool once run.

Score: The correlation score. 100% is a perfect match

Total Defect Area: The total defect area. These are returned in image units

Largest Defect Area: The Largest Defect Area

Position Misalignment: The misalignment between the trained and current position



The Results page shows the result information for each cell that was found when the template image was compared.












The Save Options page allows selection of the items to be reported.











Selecting Train will allow you to train the current image underneath the region of interest rectangle as the template image to be used for comparison.