  We propose Texture Edge detection using Patch consensus (TEP) which is a
training-free method to detect the boundary of texture. We propose a new simple
way to identify the texture edge location, using the consensus of segmented
local patch information. While on the boundary, even using local patch
information, the distinction between textures are typically not clear, but
using neighbor consensus give a clear idea of the boundary. We utilize local
patch, and its response against neighboring regions, to emphasize the
similarities and the differences across different textures. The step of
segmentation of response further emphasizes the edge location, and the
neighborhood voting gives consensus and stabilize the edge detection. We
analyze texture as a stationary process to give insight into the patch width
parameter verses the quality of edge detection. We derive the necessary
condition for textures to be distinguished, and analyze the patch width with
respect to the scale of textures. Various experiments are presented to validate
the proposed model.
