A rule-based segmentation method for fruit images under natural illumination

Image segmentation is a process that significantly important for machine vision system such as automatic fruit grading system. This process separates an image into several areas to extract the interest object from its background. However, the segmentation task is difficult for isolating the images that captured in outdoor environment. This is due to the existence of non-uniform illumination on the object surface. Technically, different illuminations lead to different intensity on the object surface colour. This condition leads to low quality segmented images and therefore reduces the accuracy of object classification. Image segmentation can be accomplished using several methods such as Otsu, K-means and Fuzzy C-means. However, these three traditional methods have limitations in producing accurate segmented areas due to the existence of illumination on the object surface. Therefore, this paper developed a rule-based segmentation method that is able to segment natural images correctly and accurately. This method uses IF-THEN algorithm to segment the images of interest object. All four segmentation methods are implemented on fruit images and their performance are compared based on visual and quantitative evaluations. The analysis results showed that the new method is capable to produce segmented images with high accuracy rate.