Journal article
Jibrin Bala, H. Salau, I. J. Umoh, A. Onumanyi, Salawudeen A. Tijani, B. Yahaya
2021
2021
Semantic Scholar
DOI
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APA
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Bala, J., Salau, H., Umoh, I. J., Onumanyi, A., Tijani, S. A., & Yahaya, B. (2021). Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image.
Chicago/Turabian
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Bala, Jibrin, H. Salau, I. J. Umoh, A. Onumanyi, Salawudeen A. Tijani, and B. Yahaya. “Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image” (2021).
MLA
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Bala, Jibrin, et al. Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image. 2021.
BibTeX Click to copy
@article{jibrin2021a,
title = {Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image},
year = {2021},
journal = {},
author = {Bala, Jibrin and Salau, H. and Umoh, I. J. and Onumanyi, A. and Tijani, Salawudeen A. and Yahaya, B.}
}
Abstract
The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.
