Viola-Jones method of marker detection for scale-invariant calculation of lettuce leaf area
College
Gokongwei College of Engineering
Department/Unit
Mechanical Engineering
Document Type
Conference Proceeding
Source Title
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
Publication Date
3-12-2019
Abstract
Leaf area can be used as a growth parameter as such it increases as the stage of lettuce progress. Consideration of scale invariance in estimating the area poses challenging machine vision problems in a smart farm setup. To address this, a marker with known size and components is utilized for the system for normalizing area measurements. This study proposes an automated object detection (marker) using Viola-Jones algorithm that uses Haar features. Based on the result of this study, a high detection rate applied to 40 test samples is obtained by using 30 positive samples and 50 negative samples. The small sample size is compensated by increased number of stages and decreased lower false positive rate for each stage. Future work includes adding training sets and using other methods such as Speeded Up Robust Features (SURF). © 2018 IEEE.
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Digitial Object Identifier (DOI)
10.1109/HNICEM.2018.8666244
Recommended Citation
Loresco, P. M., Valenzuela, I. C., Culaba, A. B., & Dadios, E. P. (2019). Viola-Jones method of marker detection for scale-invariant calculation of lettuce leaf area. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018 https://doi.org/10.1109/HNICEM.2018.8666244
Disciplines
Computer Engineering | Engineering
Keywords
Computer vision; Integrals, Haar; Image processing; Leaves—Growth
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