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Image cropping

To remove the background, and to be consistent with the experiments in [4,5], we manually cropped images to 200 x 200 pixels when possible. However, for some samples (Brown bread and Cracker B) this was not possible at large camera-target distances since the subject did not fill a sufficiently large part of the image. In these cases, images were instead cropped as follows:

  1. If possible, an ``equivalently sized'' rectangular region was selected, with an aspect ratio as close as possible to 1. ``Equivalently sized'' implies that the same number of pixels should be made available to the computer vision algorithm (e.g. material classification algorithm) once early processing (e.g. filtering) has been performed. In our work [3] the early processing involved applying a filter bank, and removing pixels which were not entirely covered by the support region of the filter kernel. These pixels are located at the edges of the image patch. In particular we used 41x41 filter kernels, so with a 200 x 200 patch, after filtering we were left with (200-40) x (200-40) = 1602=25600 pixels which were input to the classification algorithm. Therefore, we selected X x Y patches such that (X-40) x (Y-40) was approximately 25600.
  2. If the largest possible X and Y did not satisfy the ``equivalently sized'' criterion above, we simply took the largest possible rectangular region corresponding to the foreground texture.

We must emphasize that the ``equivalent size'' condition is dependent on the employed image processing strategy and might very well be poorly suited to your application.

Table 4 lists where these cropping strategies were necessary. With Brown bread the texture round the edges of the slice is somewhat different (denser) to that in the middle, so these edges were also removed.

Table 4: Images where it was not possible to extract 200 x 200 pixels foreground patches.
Material Scale Images Cropping strategy
Brown bread 6 All Equivalent size
7 8,9 Equivalent size
7 1,2,3,4,5,6,7 Largest possible
8 and 9 All Largest possible
Cracker B 7 All Equivalent size
8 1,2,3 Equivalent size
8 4,5,6,7,8,9 Largest possible
9 All Largest possible

Additionally we would like to point out that with Orange peel it was not always possible to extract 200 x 200 pixel foreground patches either. However, with this material the CUReT database exhibits similar problems; in the CUReT images some background is present. Since one of our main objectives was to attempt to recognise our samples using models trained on the CUReT database, we decided against cropping the Orange Peel to a smaller size. It is, however, undoubtedly a problem that the amount of background varies from scale to scale, and our background was not quite as uniform as the CUReT background.

next up previous
Next: Some poor quality images Up: THE KTH-TIPS database Previous: Image acquisition