  In this paper we deal with contour detection based on the recent image
analogy principle which has been successfully used for super-resolution,
texture and curves synthesis and interactive editing. Hand-drawn outlines are
initially as benchmarks. Given such a reference image, we present a new method
based on this expertise to locate contours of a query image in the same way
that it is done for the reference (i.e by analogy).
  Applying a image analogies for contour detection using hand drawn images as
leaning images cannot gives good result for any query image. The contour
detection may be improved if we increase the number of learning images such
that there will be exist similarity between query image and some reference
images. In addition of the hardness of contours drawing task, this will
increase considerably the time computation.
  We investigated in this work, how can we avoid this constraint in order to
guaranty that all contour pixels will be located for any query image. Fourteen
derived stereo patches, derived from a mathematical study, are the knowledge
used in order to locate contours at different scales independently of the light
conditions.
  Comprehensive experiments are conducted on different data sets (BSD 500,
Horses of Weizmann). The obtained results show superior performance via
precision and recall vs. hand-drawn contours at multiple resolutions to the
reported state of the art.
