Abstract
Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single global inner center and a set of radii corresponding to a polar coordinate parameterization. In this paper, we propose AdaContour, an adaptive contour descriptor that uses multiple local representations to desirably characterize complex shapes. After hierarchically encoding object shapes in a training set and constructing a contour matrix of all subdivided regions, we compute a robust low-rank robust subspace and approximate each local contour by linearly combining the shared basis vectors to represent an object. Experiments show that AdaContour is able to represent shapes more accurately and robustly than other descriptors while retaining effectiveness. We validate AdaContour by integrating it into off-the-shelf detectors to enable instance segmentation which demonstrates faithful performance. The code is available at this https URL.
Abstract (translated)
现有的基于角度的轮廓描述符在非星形凸形状上存在失真表示。总的来说，这是由于将形状与单个全局内切中心和一系列半径对应于极坐标参数化相注册的结果。在本文中，我们提出了AdaContour，一种自适应轮廓描述符，它使用多个局部表示来 desirable地描述复杂形状。在训练集中对对象形状进行层次编码，并构建了所有子分区的轮廓矩阵后，我们计算了一个鲁棒的低秩鲁棒子空间，并通过线性组合共享基础向量来近似每个局部轮廓，从而表示一个物体。实验表明，AdaContour能够比其他描述符更准确、更稳健地表示形状，同时保持有效性和精确性。通过将AdaContour集成到标准检测器中进行实例分割，我们证明了其忠实性能。代码可在此处访问：https://url.com/
URL
https://arxiv.org/abs/2404.08292
