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Automatic parameter tuning for adaptive thresholding in fruit detection
Elie Zemmour
, Polina Kurtser
,
Yael Edan
Department of Industrial Engineering and Management
Research output
:
Contribution to journal
›
Article
›
peer-review
33
Scopus citations
Overview
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Dive into the research topics of 'Automatic parameter tuning for adaptive thresholding in fruit detection'. Together they form a unique fingerprint.
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Keyphrases
Fruit Detection
100%
Adaptive Thresholding
100%
Color Space
100%
Automatic Parameter Tuning
100%
Blue-green
75%
Lighting Conditions
75%
False Positive Rate
50%
Tuning Process
50%
Apple
50%
Grape Cluster
50%
Variable Lighting
50%
Intensity-hue-saturation
50%
Small Sets
25%
Fruit Shape
25%
Noise Effects
25%
Environmental Light
25%
Detection Performance
25%
Blue Color
25%
Training Process
25%
Illumination Level
25%
Algorithm Parameters
25%
Color Dimensions
25%
Growing Conditions
25%
Illumination Conditions
25%
Robustness to Noise
25%
Yellow Pepper
25%
Tuning Procedure
25%
Four Colors
25%
Robust Detection
25%
Training Image
25%
Red Apples
25%
LAB Color Space
25%
Dynamic Adaptive
25%
Green Grape
25%
Color Illumination
25%
Normalized Difference Index
25%
Extensive Analysis
25%
Space Use
25%
Fit Value
25%
Adaptive Thresholding Algorithm
25%
Computer Science
Lighting Condition
100%
Detection Performance
33%
False Positive Rate
33%
Future Development
33%
True Positive Rate
33%
Training Process
33%
Illumination Condition
33%
RGB Image
33%
Training Image
33%
Cross-Validation
33%
F-Score
33%