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- Title
Illumination-invariant vegetation detection for a vision sensor-based agricultural applications.
- Authors
Keun Ha Choi; Soo Hyun Kim
- Abstract
In this paper, we propose a novel method, illumination-invariant vegetation detection (IVD), to improve many aspects of agriculture for vision-based autonomous machines or robots. The proposed method derives new color feature functions from simultaneously modeling the spectral prope1ties of the color camera and scene illumination. An experiment in which an image sample dataset was acquired under nature illumination, including various intensities, weather conditions, shadows and reflections, was performed. The results show that the proposed method (IVD) yields the highest performance with the lowest e1Tor and standard deviation and is superior to six typical methods. Our method has multiple strengths, including computational simplicity and uniformly high-accuracy image segmentation.
- Subjects
IMAGE segmentation; AUTONOMOUS robots; WEATHER; STANDARD deviations; NATURE; VISION; IMAGE sensors; SURGICAL robots
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2021, Vol 11, Issue 2, p1284
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v11i2.pp1284-1292