Using Threshold Methods to Segment Highly Images beans without overlapping - Saba Q. H. Al-Hashimy (PROSPECTIVE RESEARCHES, 2023)
This paper presents a proposed method for segmenting images of beans into separated objects without overlapping, based on the area of ideal grain in (perfect) beans by thresholding methods. The global thrshoding for the entire image is used at first. Then local thresholding was used to segment into sub-images, that contain overlapping between grains. Segmentation operations for the overlapping objects into separated objects without any overlapping depend on the minimum value of the histogram at first in sub-image, by repeating local thresholding until getting a value (valley between two peaks) to split two objects. Each object is compared with the ideal grain to put it in the result image. The proposed method is compared with the technique adopted in Matlab environment. By computing the average ratio of erosion for overlapping edges objects after separating in two methods, the proposed algorithm is 5% better than the traditional method of 19% that means increase the accuracy by 14%, additional clarity image (shape of grain) and the given number of objects is (44), which is exactly too.