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.

翻译：我们提出一种基于块共识的纹理边缘检测方法(TEP)，这是一种无需训练的纹理边界检测方法。我们提出一种利用分割后的局部块信息共识来识别纹理边缘位置的新型简便方法。尽管在边界处，即便使用局部块信息，不同纹理之间的区分通常也不够清晰，但通过邻域共识却能清晰界定边界。我们利用局部块及其对邻域区域的响应，来强调不同纹理间的相似性与差异性。响应分割步骤进一步凸显了边缘位置，而邻域投票则通过共识机制稳定了边缘检测。我们将纹理视为平稳过程进行分析，以深入理解块宽度参数与边缘检测质量之间的关系。我们推导了纹理可区分性的必要条件，并针对纹理尺度分析了块宽度参数。通过多种实验验证了所提模型的有效性。