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I have a question about preparing the dataset of positive samples for a cascaded classifier that will be used for object detection.

As positive samples, I have been given 3 sets of images:

  1. a set of colored images in full size (about 1200x600) with a white background and with the object displayed at a different angles in each image
  2. another set with the same images in grayscale and with a white background, scaled down to the detection window size (60x60)
  3. another set with the same images in grayscale and with a black background, scaled down to the detection window size (60x60)

My question is that in Set 1, should the background really be white? Should it not instead be an environment that the object is likely to be found in in the testing dataset? Or should I have a fourth set where the images are in their natural environments? How does environment figure into the training samples?

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I think you should have a fourth dataset where the images are in their natural environments because you will need information with white background when you will be testing the datasets.

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  • $\begingroup$ Thanks, I've been thinking about using OpenCV. I've realized I actually don't need full size images either, do I? All images will be resized anyway by the classification trainer down to detection window size, i.e. 60x60. $\endgroup$ – user961627 Nov 28 '13 at 6:47
  • $\begingroup$ What kind of object are you working with? $\endgroup$ – Aaron May 14 '14 at 15:48

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