用于奶牛乳头长度表型分析的3D乳房点云高斯过程模型
A Gaussian Process Model of 3D Udder Point Clouds for Teat Length Phenotyping in Dairy Cows
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中文总结 AI 辅助
针对奶牛乳房点云乳头标志自动检测难题，提出基于高斯过程的乳头长度估计方法，通过低秩近似降低计算复杂度，较现有方法精度更高、速度更快，鲁棒性更强，适配大规模自动化表型分析。
中文摘要 AI 辅助
体型性状的表型分析对奶牛育种和管理至关重要。尽管3D成像技术可实现大规模表型分析，但解剖学标志的手动标注及较长的运行时间阻碍了整个流程的自动化。尤其是奶牛乳房的形态异质性使得乳头标志的自动检测极具挑战性。为解决这一局限，本文提出并评估了一种基于高斯过程从乳房点云估计乳头长度的方法。我们将垂直坐标建模为代表乳房底部的高斯过程与代表乳头的未知函数之和。由于乳房底部过程是平滑的，且其依赖长度尺度显著大于乳头函数，该模型可分离出定义乳头标志所需的两个项。为确保计算可行性，我们实现了协方差矩阵的低秩近似，将方法的计算复杂度从$\boldsymbol{\textit{O}}(n^3)$降低至$\boldsymbol{\textit{O}}(n)$。该方法比现有方法更快、更准确，RMSE（均方根误差）降低了一半，且对罕见乳房形态的鲁棒性更强，更适合大量个体的自动化表型分析。
英文摘要
Phenotyping conformation traits is important for dairy cattle breeding and management. Although large-scale phenotyping is possible with 3D imaging technologies, manual annotation of anatomical landmarks and long run times prevent full pipeline automation. In particular, the morphological heterogeneity of cow udders makes automated detection of teat landmarks challenging. To address this limitation, we propose and evaluate a method for teat length estimation from udder point clouds with a Gaussian process. We model the vertical coordinates as the sum of a Gaussian process representing the udder floor and an unknown function representing the teat. Since the udder floor process is smooth and has a significantly wider dependence lengthscale than the teat function, this model allows separating the two terms needed for teat landmark definition. To ensure computational feasibility, we implement a low-rank approximation of the covariance matrix, reducing the computational complexity of the method from $\mathcal{O}(n^3)$ to $\mathcal{O}(n)$. This approach is both faster and more accurate than existing methods, reducing RMSE by a factor of two. It is also more robust to uncommon udder morphologies, making it better suited for automated phenotyping of large numbers of individuals.
