Abstract
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.
Abstract (translated)
我们提出了一种名为Texture Edge检测的Patch共识方法（TEP），这是一种无需训练的检测纹理边界的训练-免费方法。我们提出了一种新的简单方法来确定纹理边缘位置，利用分割局部补丁信息的共识。即使在边界上，即使使用局部补丁信息，纹理之间的区别通常也不是很清晰，但是使用邻居共识可以明确地得到边界。我们利用局部补丁及其对邻近区域的响应来强调不同纹理之间的相似之处和差异。对于响应的分割步骤进一步强调了边缘位置，邻居投票可以达成共识并稳定边缘检测。我们将纹理视为静止过程，以探究纹理宽度参数与边缘检测质量之间的关系。我们推导了纹理被区分的必要条件，并分析了纹理宽度与纹理规模的关系。我们展示了各种实验来验证所提出的模型。
URL
https://arxiv.org/abs/2403.11038
