Improvement Procedure for Image Segmentation of Fruits and Vegetables Based on the Otsu Method

Abstract

Currently, there are significant challenges in the classification, recognition, and detection of fruits and vegetables. An important step in solving this problem is to obtain an accurate segmentation of the object of interest. However, the background and object separation in a grayscale image shows high errors for some thresholding techniques due to uneven or poorly conditioned lighting. An accepted strategy to reduce segmentation errors is to select the channel of an RGB image with high contrast. This paper presents the results of an experimental procedure based on enhancing binary segmentation by using the Otsu method. The procedure was carried out with images of real agricultural products, both with and without additional noise, to corroborate the robustness of the proposed strategy. The experimental tests were performed using our database of RGB images of agricultural products under uncontrolled illumination. The results show that the best segmentation is achieved by selecting the Blue channel of the RGB test images due to its higher contrast. Here, the quantitative results are measured by applying the Jaccard and Dice metrics based on the ground-truth images as optimal reference. Most of the results using both metrics show an improvement greater than 45.5% in the two experimental tests.

Keywords

• fruits and vegetable images
• segmentation
• otsu
• contrast
• illumination improvement