论文标题
光谱量级：用于猪肉切割重量预测的光谱图小波框架
SpectralWeight: a spectral graph wavelet framework for weight prediction of pork cuts
论文作者
论文摘要
在本文中，我们提出了一种使用3D形状分析的新方法来评估猪尸体的质量评估。首先，我们使用3D扫描仪制作猪肉半键盘的3D模型，然后利用光谱图小波签名（SGW）来构建局部光谱描述符。接下来，我们使用几何词范式汇总提取的特征，以全球代表半键盘形状。然后，我们采用部分最小二乘回归，以预测猪肉切割的质量评估的重量。我们的结果表明，光谱量可以高精度预测不同猪肉切割和组织的重量。尽管在这项研究中，我们评估了SGW的性能对猪排的重量预测，但我们的框架相当一般，并可以估算不同动物尸体的质量和经济价值的新方法。
In this paper, we propose a novel approach for the quality assessment of pork carcasses using 3D shape analysis. First, we make a 3D model of a pork half-carcass using a 3D scanner and then we take advantage of spectral graph wavelet signature (SGWS) to build a local spectral descriptor. Next, we aggregate the extracted features using the bag-of-geometric-words paradigm to globally represent the half-carcass shape. We then employ partial least-squares regression to predict the weight of pork cuts for the quality assessment of carcasses. Our results demonstrate that SpectralWeight can predict the weight of different pork cuts and tissues with high accuracy. Although in this study we evaluate the performance of SGWS for the weight prediction of pork dissection, our framework is fairly general and enables new ways to estimate the quality and economical value of carcasses of different animals.
