Abstract
Rotation-invariant recognition of shapes is a common challenge in computer vision. Recent approaches have significantly improved the accuracy of rotation-invariant recognition by encoding the rotational invariance of shapes as hand-crafted image features and introducing deep neural networks. However, the methods based on pixels have too much redundant information, and the critical geometric information is prone to early leakage, resulting in weak rotation-invariant recognition of fine-grained shapes. In this paper, we reconsider the shape recognition problem from the perspective of contour points rather than pixels. We propose an anti-noise rotation-invariant convolution module based on contour geometric aware for fine-grained shape recognition. The module divides the shape contour into multiple local geometric regions(LGA), where we implement finer-grained rotation-invariant coding in terms of point topological relations. We provide a deep network composed of five such cascaded modules for classification and retrieval experiments. The results show that our method exhibits excellent performance in rotation-invariant recognition of fine-grained shapes. In addition, we demonstrate that our method is robust to contour noise and the rotation centers. The source code is available at this https URL.
Abstract (translated)
旋转不变形状识别是计算机视觉中的一个常见挑战。近期的方法通过编码手工设计的图像特征来改进旋转不变性，这些特征描述了形状的旋转不变性，并引入了深度神经网络以提高准确性。然而，基于像素的方法包含了大量的冗余信息，关键几何信息容易过早丢失，这导致对细粒度形状进行弱旋转不变性识别的问题。 在本文中，我们重新考虑从轮廓点而非像素的角度来解决形状识别问题。为此，我们提出了一种抗噪的、基于轮廓几何感知的旋转不变卷积模块（Rotation-invariant Convolution Module with Contour Geometric Awareness, RC-MCGA），以用于细粒度形状识别。该模块将形状轮廓划分为多个局部几何区域(LGA)，并在点拓扑关系的基础上实现了更细化的旋转不变性编码。我们提供了一个由五个这样的级联模块组成的深度网络，用于分类和检索实验。实验结果表明，我们的方法在细粒度形状的旋转不变性识别中表现出色。 此外，我们还证明了该方法对轮廓噪声和旋转中心具有鲁棒性，并且源代码可在以下链接获得：[提供具体URL]。
URL
https://arxiv.org/abs/2503.10992
