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A Lawn Deterioration Model Constructed from Image Data

A Lawn Deterioration Model Constructed from Image Data. Yurie Enomoto, Chisato Ishikawa, Masami Takata, Kazuki Joe Department of Advanced Information & Computer Sciences, Nara Women’s University, Nara, Japan. Contents. Background Image Analysis for Lawns Sprayed with Paint

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A Lawn Deterioration Model Constructed from Image Data

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  1. A Lawn Deterioration Model Constructed from Image Data Yurie Enomoto, Chisato Ishikawa, Masami Takata, Kazuki Joe Department of Advanced Information & Computer Sciences, Nara Women’s University, Nara, Japan

  2. Contents • Background • Image Analysis for Lawns Sprayed with Paint • Analysis by RGB and Model Construction • Analysis by HSV and Model Construction • Image Analysis for Lawns Sprinkled with Water • Analysis by RGB and Model Construction • Analysis by HSV and Model Construction • Conclusions and Future works

  3. Background Periodical mowing →Affect the quality of lawns Keep the quality of lawns Improve just color of lawns →Green paint spraying on the lawn with degraded leaf color ~Advantage~ A low cost technique Simple operations Before After Necessity of a model to understand the relation between the durability of lawns color and the paint density

  4. Lawn Images For deterioration models of lawns sprayed with paint 1) Before spraying paint 2) Just after spraying paint 3) 40 minutes later 4) 8 days later 5) 11 days later ① 6) 11 days later ②

  5. Lawn Images For deterioration model of water-sprinkled lawns 1) Right after water-sprinkled (3 pieces) 2) 8 days later (3 pieces) 3) 16 days later (3 pieces) 4) 21 days later (3 pieces) 5) 28 days later (3 pieces)

  6. Image Analysis for Lawns Sprayed with Paint RGB values Gaussian distribution • Pixel value in the top of graph （Central value） →The maximum number of pixels • The width from the central value (Dispersion width) →The dispersion of density value

  7. Image Analysis for Lawns Sprayed with Paint Analysis by dispersion widths of RGB

  8. Image Analysis for Lawns Sprayed with Paint Analysis by dispersion widths of RGB ＜After 8 days＞ • R・B ： Expansion of dispersion 　 →Degradation of the lawns

  9. Image Analysis for Lawns Sprayed with Paint Analysis by dispersion widths of RGB ＜After 8 days＞ • R・B ： Expansion of dispersion 　 →Degradation of the lawns • G ： Smaller dispersion than R,B →Controlled deterioration of lawns color

  10. Model Construction＜Ｒ・Ｂ＞ Sigmoid function a: 0.5 a: 1.0 a: 1.5 （1） （2） A fractional function a: 1.5 a: 1.0 a: 0.5 Increase to a certain value to converge

  11. Model Construction＜Ｒ・Ｂ＞ 30 28 28 26 26 24 24 22 22 20 20 18 18 Analysis result by R Model expression for R 16 Analysis result by B Model expression for B 16 14 R： B：

  12. Model Construction＜Ｇ＞ Expression(3): Logarithm based function Expression(4)(5): Exponential based function a: 5 a: 10 a: 15 a: 0.5 a: 1.0 a: 1.5 A function with a peak of enlarged dispersion a: 1.0, b: 0.5 a: 1.0, b: 1.0 a: 1.0, b: 1.5 a: 0.5, b: 1.0 a: 1.0, b: 1.0 a: 1.5, b: 1.0 Change by coefficient a Change by coefficient b

  13. 28 26 24 22 20 18 16 Model Construction＜Ｇ＞ Expression(3): Logarithm based function Expression(4)(5): Exponential based function a: 5 a: 10 a: 15 a: 0.5 a: 1.0 a: 1.5 A function with a peak of enlarged dispersion a: 1.0, b: 0.5 a: 1.0, b: 1.0 a: 1.0, b: 1.5 a: 0.5, b: 1.0 a: 1.0, b: 1.0 a: 1.5, b: 1.0 Analysis result by G Model expression for G Change by coefficient a Change by coefficient b G：

  14. Image Analysis for Lawns Sprayed with Paint Analysis by dispersion widths of HSV

  15. Image Analysis for Lawns Sprayed with Paint Analysis by dispersion widths of HSV • H : Expansion of dispersion →Expansion of the range of green in the hue circle 　→Increase of the number of color hue

  16. Image Analysis for Lawns Sprayed with Paint Analysis by dispersion widths of HSV • H : Expansion of dispersion →Expansion of the range of green in the hue circle →Increase of the number of color hue • S・V : Expansion of dispersion 8 days later →Dark lawns color

  17. Model Construction＜H・S・V＞ （1） a: 0.5 a: 1.0 a: 1.5 （2） a: 1.5 a: 1.0 a: 0.5 ※p.9

  18. 60 56 55 54 50 52 50 45 40 48 46 35 30 44 42 25 Model Construction＜H・S・V＞ 48 46 44 42 40 38 36 Analysis result by H Model expression for H Analysis result by S Model expression for S Analysis result by V Model expression for V 34 32 H： S： V：

  19. Image Analysis for Lawns Sprinkled with Water Binomial distribution RGB values Fresh (green) part Dried-up (white) part Analysis

  20. Image Analysis for Lawns Sprinkled with Water Analysis by dispersion widths of RGB • RGB：Expansion of dispersion from 8 days later to 16 days later →Quick deterioration of green part →Gentle gradient of Gaussian distribution • R ： The most deterioration

  21. Model Construction＜R・G・B＞ （1） a: 0.5 a: 1.0 a: 1.5 （2） a: 1.5 a: 1.0 a: 0.5 ※p.9

  22. 60 55 50 45 40 35 28 70 30 60 26 25 24 50 22 40 20 30 20 18 10 16 14 Model Construction＜R・G・B＞ Analysis result by R Model expression for R Analysis result by G Model expression for G Analysis result by B Model expression for B 20 R： G： B：

  23. Image Analysis for Lawns Sprinkled with Water Analysis by dispersion widths of HSV

  24. Image Analysis for Lawns Sprinkled with Water Analysis by dispersion widths of HSV • H・S ： Reduction of dispersion 8 days later →Dispersion on green and yellow part

  25. Image Analysis for Lawns Sprinkled with Water Analysis by dispersion widths of HSV • H・S ： Reduction of dispersion 8 days later →Dispersion on green and yellow part • V：Expansion of dispersion →Deterioration of green part →Gentle gradient of Gaussian distribution

  26. Model Construction＜V＞ （1） a: 0.5 a: 1.0 a: 1.5 （2） a: 1.5 a: 1.0 a: 0.5 ※p.9

  27. 50 45 40 35 30 25 20 Model Construction＜V＞ Analysis result by V Model expression for V V：

  28. Model Construction＜H・S＞ Expression(6): Exponential based function Expression(7): A decreasing function a: 5, b: 5 a: 10, b: 5 a: 15, b: 5 a: 0.5, b: 5 a: 0.5, b: 10 a: 0.5, b: 15 Change by coefficient a Change by coefficient b Decrease by a certain value to converge a: 0.5, b: -0.5 a: 1.0, b: -0.5 a: 1.5, b: -0.5 a: 1.5, b: -0.5 a: 1.5, b: -1.0 a: 1.5, b: -1.5 Change by coefficient a Change by coefficient b

  29. 95 90 85 80 75 70 65 Model Construction＜H・S＞ 130 Analysis result by H Model expression for H Analysis result by S Model expression for S 120 110 100 90 80 70 60 50 40 30 H： S：

  30. 60 50 40 30 20 Sprinkled with water Sprayed with paint 10 Conclusions and Future Works <Model for G> • Construct lawn deterioration models by image data Difference 35 • Future work More exact model construction by aggregate of a botanical model

  31. Thank you for your attention.

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