We introduce a bottom-up model for jointly finding many boundary elements in an image, including edges, curves, corners and junctions. The model explains boundary shape in each small patch using a junction with M angles and a freely-moving vertex. Images are analyzed by solving a non-convex optimization problem using purposefully-designed algorithms, cooperatively finding M+2 junction values at every pixel. The resulting field of junctions is simultaneously an edge detector, a corner/junction detector, and a boundary-aware smoothing of regional appearance. We demonstrate how it behaves at different scales, and for both single-channel and multi-channel input. Notably, we find it has unprecedented resilience to noise: It succeeds at high noise levels where previous methods for segmentation and for edge, corner and junction detection fail.
翻译：我们引入一个自下而上的模式, 以共同在图像中找到许多边界元素, 包括边缘、 曲线、 角和交叉点。 该模型使用与 M 角度和自由移动的顶点的连接点来解释每个小片段的边界形状。 图像通过使用特意设计的算法解决非convex优化问题, 合作在每像素中寻找 M+2 交叉值来进行分析。 由此形成的交叉点领域同时是一个边缘探测器、 角/ 枢纽探测器, 以及一个区域外观的边界辨识平滑。 我们演示它在不同尺度上的表现, 以及单通道和多通道的输入 。 值得注意的是, 我们发现它具有前所未有的对噪音的抵抗力: 它在高噪声水平上成功, 以前的分割和边缘、 角和交界点探测方法都失败 。
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