Abstract:In food production, the numerous kinds of foods produced have to meet the applicable national food safety standards. Therefore, methods for food image segmentation should be rapid with high accuracy and high universality. In food image segmentation methods based on the threshold of two-dimensional Arimoto entropy or two-dimensional Arimoto gray entropy, the algorithm is highly complex. Hence, it is difficult to meet real-time requirements. To solve this problem, a method using line intercept histogram-based Arimoto entropy or Arimoto gray entropy for food image segmentation was proposed. First, the line intercept histogram was defined, which was followed by building of the line intercept histogram of images according to this definition. Finally, Arimoto entropies or Arimoto gray entropies of different gray levels in this line intercept histogram were calculated. When the maximum entropy was reached, the corresponding grayscale was determined to be the optimal image segmentation threshold. Several experiments were performed on different kinds of food images by using this method. Compared with the existing segmentation methods based on one-dimensional and two-dimensional Arimoto entropy and Arimoto gray entropy, the method proposed here can achieve better performance by increasing algorithm speed and improving segmentation results.
