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The Orange Objects Classification Process - Attributes, Form Factor, and Eigen Values Analysis

This process involves getting images of objects marked by a robot, calculating attributes like width, height, area, and perimeter. It uses HUE color model to identify orange objects and analyzes Form Factor and Eigen Values to classify objects based on their characteristics. The decision tree methodology involves training data, pruning, and analyzing attribute combinations. Experiment with machine learning tool C4.5 and practice with Matlab lead to more insights in this classification process.

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The Orange Objects Classification Process - Attributes, Form Factor, and Eigen Values Analysis

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  1. The Orange Objects Classification

  2. My process • Get the image from the robot • Mark the position of the ball in each image • Calculate all 7 attributes • Width • Height • Area • Perimeter • Form Factor • Distance • Eigen Value

  3. Original Image

  4. Mark the position of the ball

  5. HUE color model

  6. Orange objects in HUE

  7. Discard Big objects

  8. Find interested attribute • Width, Height, and Area Area Height Width

  9. Find interested attributes • Perimeter = ( * # of Diagonal lines) + # of Straight lines From this image: # of Diagonal line = 45 lines # of Straight line = 80 lines Perimeter = 143.6396

  10. Perimeter = 18* +28 = 53.4558 A P FF. Eig Dist. W H A P FF. Eig Dist.

  11. Find interested attributes • Form Factor (FF.) = where A = area, and P = perimeter If FF. is or close to 1  the circle object

  12. Form Factor (FF.)

  13. How to calculate the perimeter

  14. = 1 diagonal line and 1 straight line

  15. = 1 diagonal line

  16. = 2 diagonal lines

  17. = 1 straight line

  18. = 2 straight lines

  19. Old Eigen Value We set the threshold value as EigVal1> (0.75*EigVal2)  It’s a ball

  20. New Eigen Value (let C4.5 learn it) • Eigen Value ratio (Eig) Eig = 1st Eigen value / 2nd Eigen value Eig Eig

  21. Find interested attributes • Centroids and Distance

  22. C4.5 result 1st combination 2nd combination 1545 986 986 Test Data Training Data 1545 986 986 New training Data New test Data

  23. Decision tree (7 attributes)size after pruning: 59 nodes <=0.8879 >0.8879 <=0.4753 >0.4753 <=16 >16 <=338.956 >338.956 <=362.988 >362.988 <=357.142 >357.142 >0.9053 <=0.9053 <=370.901 >370.901 <=0.7965 >0.7965 <=10 >10 <=0.8812 >0.8812 >379.228 <=379.228 <=64 >64 <=0.8379 >0.8379 >380.495 <=380.495 <=9 >9 <=0.5609 >0.5609 <=4468 >4468 >13 <=13 <=0.5262 >0.5262 <=376.788 >376.788 <=13 >13 <=14 <=0.6013 >0.6013 >85 <=85 <=364.569 >364.569 <=80 >80

  24. Conclusion • Gained more experiment with machine learning, C4.5 • More practice with Matlab

  25. Perimeter = 18* +28 = 53.4558 A P FF. Eig Dist. W H A P FF. Eig Dist.

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