  This paper presents novel methods to predict the surface and volume of the
ham through a camera. This implies that the conventional weight measurement to
obtain in the object's volume can be neglected and hence it is economically
effective. Both of the measurements are obtained in the following two ways:
manually and automatically. The former is assume as the true or exact
measurement and the latter is through a computer vision technique with some
geometrical analysis that includes mathematical 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 on
ham with deep learning features. We address the estimation task with a Mask
Region-based CNN (Mask R-CNN) approach, which well performs the ham detection
and semantic segmentation from a video. The experimental results demonstrate
that the algorithm proposed is robust as promising surface area and volume
estimation are obtained for two angles of the ellipsoidal ham (i.e., horizontal
and vertical positions). Specifically, in the vertical ham point of view, it
achieves an overall accuracy up to 95% whereas the horizontal ham reaches 80%
of accuracy.
