Compartmentalisation of Food Products using Color Image Segmentation

The life span of humans depends heavily on health. Many countries use various technologies to prepare and store foods. Food preparation and storage becomes an essential part for every individual to maintain their health. Compartmentalisation of food based on color is largely used in food industry. Since the visual appearance of food and other biological products is a major factor in the judgment of quality, classification is an important part of quality control in these industries. This classification has historically been performed by use of the only “tool� available, the human eye. In this paper, we start with image acquisition and next, a technique of color image segmentation, which is most inexpensive, and then an in-depth analysis system for food samples. This method uses an edge detection segmentation algorithm in which the color images of food samples are grouped into similar color characteristics. Before the segmentation, the application of Gaussian Low pass filter becomes necessary for the image to blur sharp edges. After that, each part is individually analyzed with its characteristics of color and compared against a given appropriate profile for the particular food sample. Due to the false background of pixels, the algorithm was able to establish the amount of color changes in a given food sample with minuteness and with least error. In this paper, we also present a primary calibrated setup which we have carried out for food sample collection that is free from any effect on environmental lighting conditions. We have tested our segmentation and rejection process with several different food samples and compared the results with visual inspection results. The results of the comparison were quite good. In this paper, we have presented the result that we have obtained using a wheat bread.