Year: 2019
Type: article
Abstract: This article presents novel methods to predict the surface area and volume of ham through a camera. By doing so, conventional methods of obtaining volume through measuring weight can be neglected, as they are not very economically effective. Both the surface area and volume are obtained in the following two ways: manually and automatically. The former is assumed as the true or exact measurement and the latter is done through a computer vision technique together with some geometrical analysis that includes mathematically‐derived functions. For the automatic implementation, most of the existing approaches extract the features of the food material based on handcrafted features and to the best of our knowledge, this is the first attempt to estimate the surface area and volume of ham with deep learning features. We address the estimation task with a Mask Region‐based CNN (Mask R‐CNN) approach, which will perform the ham detection and semantic segmentation from a video. The experimental results demonstrate that the algorithm proposed is robust and promising as surface area and volume estimation are obtained from two angles of the ellipsoidal ham (i.e., horizontal and vertical positions). Specifically, when the ham is measured from a vertical angle, the overall accuracy achieved is up to 95% whereas from the horizontal angle, accuracy is around 80%. Practical Application Predict the surface area and volume of the ham through a 2D camera. Robustness on automatic object segmentation for high accuracy measurement of object's volume by adopting deep learning algorithms.
DOI: 10.1111/jfpe.13093
Authors: Sze‐Teng Liong, Yee‐Siang Gan, Yen‐Chang Huang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
Funder: Xinyang Normal University
OpenAlex ID: W2963101436