Segmentation of White Chali Arecanuts using Soft Computing Methods

Abstract

Performance of computer vision based grading systems is remarkably affected by the efficiency of object segmentation. The automatic segmentation of low contrast objects is a challenging task in various fruit and nut grading systems. In this paper background elimination of white chali arecanut images is carried out using morphological segmentation. The fine-tuning of edge threshold for morphological segmentation is achieved by obtaining threshold values from multilevel thresholding of original grayscale image. The best figure ground segmentation is selected by a network trained using shape parameters of the ground truth masks. The performance of morphological segmentation is evaluated for the best figure ground segmentations using precision, recall and F-scores. Comparison of segmentation performance is done by employing multilevel thresholding based on Otsu, Fuzzy c-mean, Harmony search, Differential Evolution and Cuckoo Search algorithms. The experimental result shows that, multilevel thresholding using Differential Evolution and Cuckoo Search algorithms yield best results for the fine-tuning of edge thresholds and hence the better segmentation performance of the white chali arecanuts.