Abstract
Elliptic Fourier analysis (EFA) is a powerful tool for shape analysis, which is often employed in geometric morphometrics. However, the normalization of elliptic Fourier descriptors has persistently posed challenges in obtaining unique results in basic contour transformations, requiring extensive manual alignment. Additionally, contemporary contour/outline extraction methods often struggle to handle complex digital images. Here, we reformulated the procedure of EFDs calculation to improve computational efficiency and introduced a novel approach for EFD normalization, termed true EFD normalization, which remains invariant under all basic contour transformations. These improvements are crucial for processing large sets of contour curves collected from different platforms with varying transformations. Based on these improvements, we developed ElliShape, a user-friendly software. Particularly, the improved contour/outline extraction employs an interactive approach that combines automatic contour generation for efficiency with manual correction for essential modifications and refinements. We evaluated ElliShape's stability, robustness, and ease of use by comparing it with existing software using standard datasets. ElliShape consistently produced reliable reconstructed shapes and normalized EFD values across different contours and transformations, and it demonstrated superior performance in visualization and efficient processing of various digital images for contour this http URL output annotated images and EFDs could be utilized in deep learning-based data training, thereby advancing artificial intelligence in botany and offering innovative solutions for critical challenges in biodiversity conservation, species classification, ecosystem function assessment, and related critical issues.
Abstract (translated)
椭圆傅里叶分析（EFA）是形状分析的强大工具，常用于几何形态学中。然而，在进行基本轮廓变换时，椭圆傅里叶描述符的标准化一直面临着挑战，需要大量的手动对齐来获得独特结果。此外，现代轮廓/边缘提取方法在处理复杂数字图像方面常常遇到困难。在此基础上，我们重新制定了EFDs计算程序以提高计算效率，并引入了一种新的规范化方法——真正的EFD规范化，它在所有基本轮廓变换下保持不变。这些改进对于处理从不同平台收集并经历各种转换的大规模轮廓曲线集至关重要。基于这些改进，我们开发了ElliShape这一用户友好的软件。特别地，改进的轮廓/边缘提取采用了一种结合自动轮廓生成以提高效率和手动校正进行必要修改和细化的交互式方法。通过使用标准数据集与现有软件进行比较，评估了ElliShape的稳定性、鲁棒性和易用性。结果显示，ElliShape能够始终如一地生成可靠的重建形状和标准化后的EFD值，并在不同轮廓和变换下表现出卓越的可视化能力和各种数字图像处理效率。由此产生的带注释的图像和EFDs可用于基于深度学习的数据训练，从而推动植物学领域的人工智能发展，并为生物多样性保护、物种分类、生态系统功能评估等关键问题提供创新解决方案。
URL
https://arxiv.org/abs/2412.10795
