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 Width Height Area

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

10. Perimeter = 18* +28 = 53.4558 WHAPFF.EigDist.APFF.EigDist.

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.) BlueYellow Area121 Perim.4044 FF.0.9750.886

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 EigVal 1 > (0.75*EigVal 2 )  It’s a ball

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

21. Find interested attributes Centroids and Distance

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

23. Decision tree (7 attributes) size after pruning: 59 nodes <=306.358>306.358 <=29>29 >14 <=0.8879 <=0.4753>0.4753 <=0.7965>0.7965 <=64>64 <=0.5609>0.5609<=4468 >4468 <=357.142>357.142 <=10 >10 <=0.8379>0.8379 >13<=13 <=14 <=0.8812>0.8812 >380.495<=380.495 <=0.5262>0.5262 <=0.6013>0.6013 >0.8879 <=16>16 <=338.956>338.956 >0.9053<=0.9053 >379.228<=379.228 <=9>9 <=376.788>376.788<=13>13 >85<=85 <=80>80 <=364.569>364.569 <=362.988>362.988 <=370.901>370.901

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

26. Perimeter = 18* +28 = 53.4558 WHAPFF.EigDist.APFF.EigDist.