Segmentation of White Chali Arecanuts using Soft Computing Methods
Kusumadhara S1, Ravikumar M S2
1Kusumadhara S, Department of Electronics and Communication Engineering, K V G College of Engineering, Sullia and affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
2Ravikumar M S, Department of Electronics and Communication Engineering, K V G College of Engineering, Sullia and affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 29, 2020. | Manuscript published on May 10, 2020. | PP: 1049-1055 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5848059720/2020©BEIESP | DOI: 10.35940/ijitee.G5848.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Morphological Segmentation, Multilevel Thresholding, Soft computing method, White Chali Arecanut.
Scope of the Article: Soft computing Signal and Speech Processing