  We present AMOS Patches, a large set of image cut-outs, intended primarily
for the robustification of trainable local feature descriptors to illumination
and appearance changes. Images contributing to AMOS Patches originate from the
AMOS dataset of recordings from a large set of outdoor webcams.
  The semiautomatic method used to generate AMOS Patches is described. It
includes camera selection, viewpoint clustering and patch selection. For
training, we provide both the registered full source images as well as the
patches.
  A new descriptor, trained on the AMOS Patches and 6Brown datasets, is
introduced. It achieves state-of-the-art in matching under illumination changes
on standard benchmarks.
