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A statistical approach in enhancing the volume prediction of ellipsoidal ham
Journal of Food Engineering ( IF 5.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.jfoodeng.2020.110186 Y.S. Gan , Lan Wei , Yiming Han , Chenyu Zhang , Yen-Chang Huang , Sze-Teng Liong
Journal of Food Engineering ( IF 5.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.jfoodeng.2020.110186 Y.S. Gan , Lan Wei , Yiming Han , Chenyu Zhang , Yen-Chang Huang , Sze-Teng Liong
Abstract In literature, there exist many attempts to determine the surface area and volume of an irregular object using automated image processing techniques. This paper expanded previous work on predicting the volume of ellipsoidal hams by using both image processing techniques and numerical methods. Novel algorithms were proposed to improve the prediction accuracy and robustness of the volume estimation mechanism. Particularly, the work focused on the ham’s position in the horizontal viewpoint. An industrial robotic arm was utilized to lift the ham object and rotate it at a fixed controlled speed to maximize data consistency. Then, a Mask Region-based convolutional neural network approach was used to extract the ham object’s features. Experiments were conducted on 16 newly collected ham datasets. In this paper, performance comparisons between this and the previous work were reported and detailed analyses presented. Particularly, three numerical algorithms (i.e., based on the minor axis, Y-direction, and k-nearest neighbor) were introduced to enhance volume prediction in the two databases. The new algorithm exhibited a 27% higher performance than that of the previous work’s algorithm. Related theoretical and conceptual frameworks were discussed to further provide evidence and insights on the proposed mechanism.
中文翻译:
一种增强椭圆火腿体积预测的统计方法
摘要 在文献中,有许多尝试使用自动图像处理技术来确定不规则物体的表面积和体积。本文扩展了之前通过使用图像处理技术和数值方法预测椭圆体火腿体积的工作。提出了新的算法来提高体积估计机制的预测精度和鲁棒性。特别是,这项工作集中在火腿在水平视点中的位置。工业机械臂用于提升火腿物体并以固定的受控速度旋转,以最大限度地提高数据一致性。然后,使用基于掩码区域的卷积神经网络方法来提取 ham 对象的特征。对 16 个新收集的火腿数据集进行了实验。在本文中,报告了这项工作与之前工作之间的性能比较,并提供了详细的分析。特别地,引入了三种数值算法(即基于短轴、Y 方向和 k 最近邻)来增强两个数据库中的体积预测。新算法的性能比之前工作的算法高出 27%。讨论了相关的理论和概念框架,以进一步提供有关拟议机制的证据和见解。
更新日期:2021-02-01
中文翻译:
一种增强椭圆火腿体积预测的统计方法
摘要 在文献中,有许多尝试使用自动图像处理技术来确定不规则物体的表面积和体积。本文扩展了之前通过使用图像处理技术和数值方法预测椭圆体火腿体积的工作。提出了新的算法来提高体积估计机制的预测精度和鲁棒性。特别是,这项工作集中在火腿在水平视点中的位置。工业机械臂用于提升火腿物体并以固定的受控速度旋转,以最大限度地提高数据一致性。然后,使用基于掩码区域的卷积神经网络方法来提取 ham 对象的特征。对 16 个新收集的火腿数据集进行了实验。在本文中,报告了这项工作与之前工作之间的性能比较,并提供了详细的分析。特别地,引入了三种数值算法(即基于短轴、Y 方向和 k 最近邻)来增强两个数据库中的体积预测。新算法的性能比之前工作的算法高出 27%。讨论了相关的理论和概念框架,以进一步提供有关拟议机制的证据和见解。