Computer vision performance metrics evaluation of object detection based on Haar-like, HOG and LBP features for scale-invariant lettuce leaf area calculation
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2019-02-26 
https://doi.org/10.14419/ijet.v7i4.26071
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Haar-Like, HOG, LBP Features, Lettuce Leaf Area, Scale Invariance, Viola-Jones Algorithm. -
Abstract
Leaf area can be used as a growth parameter as such it increases as the stage of lettuce progresses. Consideration of scale invariance in estimating the area poses challenging machine vision problems in a smart farm setup. To address this, a marker with a known area is utilized for the system for normalizing area measurements. This study proposes an automated object detection (marker) using Viola-Jones algorithm that uses Haar-like, HOG and LBP features. Performances of the system using each feature at varying illuminations and distances are then compared. Based on the result of this study, the highest performance in general, based on accuracy, precision, and false positive rate is achieved using HOG features.
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References
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How to Cite
James Loresco, P., Bandala, A., Culaba, A., & Dadios, E. (2019). Computer vision performance metrics evaluation of object detection based on Haar-like, HOG and LBP features for scale-invariant lettuce leaf area calculation. International Journal of Engineering & Technology, 7(4), 4866-4872. https://doi.org/10.14419/ijet.v7i4.26071Received date: 2019-01-18
Accepted date: 2019-02-11
Published date: 2019-02-26