This site is devoted to mathematics and its applications. Created and run by Peter Saveliev.

Analysis of sample images

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During processing, Pixcavator computes the areas, perimeters, and contrasts of the objects. These parameters are used filter objects. The user indicates ranges of parameters of the features he considers to be irrelevant or noise.

Coins.
The coins are captured.

The settings may be chosen based on a priori knowledge about the image. For example the image below is 300×246. To capture the coins and ignore the noise and the small features depicted on the coins, one sets the lower limit for the area at 1000 pixels.





Run this analysis with Pixcavator SI.

Coins blurred.
The coins are captured.











Coins with Gaussian noise.
The coins are captured.











Fingerprint
Fingerprint rotated

The analysis is not significantly affected by rotations. The output for the original 640×480 fingerprint is 3121 dark and 1635 light objects. For the rotated version, it is 2969-1617. By limiting the analysis to objects with area above 50 pixels, the results are improved to 265-125 and 259-124, respectively.





Another example is counting and measuring these seeds:

Seeds.jpg

Run this analysis with Pixcavator SI.

Stretching the image does not affect the count of objects. Shrinking makes objects merge. If the goal, however, is to count and analyze larger features, limited shrinking of the image does not affect the outcome. The count is also stable under noise and blurring.

The method works best with images that represent something 2-dimensional. It fails when the third dimension is essential. It is the case when the images contain:

  • occluded objects,
  • transparent objects,
  • objects well lit on one side and dark on the other,
  • X-rays.

Another limitation of the method is that clustered cells or other objects can’t be separated unless there is variation of intensity (see Binary watershed).






Other examples of image analysis