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 to solve this problem is to obtain an accurate segmentation of the object of interest. However, the background and object separation in a gray image shows high errors for some thresholding techniques due to uneven or poorly conditioned lighting. An accepted strategy to decrease the error segmentation is to select the channel of a RGB image with high contrast. This paper presents the results of an experimental procedure based on a binary segmentation enhancement by using the Otsu method. The procedure was carried out with images of real agricultural products with and without additional noise to corroborate the robustness of the proposed strategy. The experimental tests were performed by using our database of RGB images of agricultural products under uncontrolled illumination. The results exhibit that the best segmentation is based on the selection of the Blue channel of the RGB test images due to its better 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 show an average percentage improvement difference greater than 45.5% in two experimental tests.