An Multiple Regression Analysis Based Color Transform Between Objects

1. An Multiple Regression Analysis Based Color Transform Between Objects Speaker：Chen-Chung Liu

2. Outline • Introduction • The proposed algorithm • Color Objects Extraction Algorithm Using Multiple Thresholds (COEMT) • Color Transform Using Multiple Regression Analysis (MRA) • Conclusions

3. 1. Introduction(1/3) • Art purpose

4. 1. Introduction (2/3) • Image analysis (details increasing)

5. 1. Introduction (3/3) • Image analysis (image simplify)

6. The proposed algorithm Figure 1. The flow chart of the proposed color transformation algorithm.

7. 2.1.Color Objects Extraction(1/17) Figure 2. Color objects extraction algorithm flow chart.

8. Figure 3. Pixels values distribution on different planes. 2.1.Color Objects Extraction (2/17)

9. 2.1.Color Objects Extraction (3/17) Figure 4. Intensity versus RGB and saturation versus RGB.

10. 2.1.Color Objects Extraction (4/17) Figure 5. The flow chart of EOAFF on HSI domain.

11. 2.1.Color Objects Extraction(5/17)

12. 2.1.Color Objects Extraction(6/17)

13. 2.1.Color Objects Extraction(7/17)

14. 2.1.Color Objects Extraction(8/17)

15. 2.1.Color Objects Extraction(9/17) • Filter’s thresholds of hue , saturation , and intensity

16. 2.1.Color Objects Extraction(10/17) Figure 6. An example of the proposed adaptive forecasting filter‘s working.

17. 2.1.Color Objects Extraction(11/17) union result original image CS result BSE result Figure 7. An example of the proposed scheme.

18. 2.1.Color Objects Extraction(12/17) original imagewith seeds DTS in RGB DTS in HSI proposed scheme Figure 8. Test image: Pink hat. C. C. Liu and G. N. Hu, Color Objects Extraction Scheme Using Dynamic Thresholds (DTS), 2009 Workshop on Consumer Electronics (WCE2009), pp. 1130-1138, 2009.

19. 2.1.Color Objects Extraction(13/17) original imagewith seeds DTS in RGB DTS in HSI proposed scheme Figure 9. Test image: Flowers.

20. 2.1.Color Objects Extraction(14/17) original image with seeds DTS in RGB DTS in HSI proposed scheme Figure 10. Test image: Pottery.

21. 2.1.Color Objects Extraction(15/17) original image with seeds DTS in RGB DTS in HSI proposed scheme Figure 11. Test image: Cup set.

22. 2.1.Color Objects Extraction(16/17) DTS in RGB original image with seeds DTS in HSI proposed scheme Figure 12. Test image: Sun flower.

23. 2.1.Color Objects Extraction(17/17) Table 1. Comparisons of extraction results

24. 2.2. MRA_based Color Transform(1/20) Multiple Regression Analysis (1/5) For data of ordered pairs We want to predict y from x by finding a function that fits the data as closely as possible.

25. 2.2. MRA_based Color Transform(2/20) Multiple Regression Analysis (2/5) • MRA is used to find a polynomial function of degree , as the predicting function, that has the minimum of the sum of squares of the errors(SSE) between the predicted values of y and the observed values for all of the n data points .

26. 2.2. MRA_based Color Transform(3/20) Multiple Regression Analysis (3/5) • The values of , , ,…,and that minimizeare obtained by setting the first partial derivatives , ,…, andequal to zero.

27. 2.2. MRA_based Color Transform(4/20) Multiple Regression Analysis (4/5) • Solving the resulting simultaneous linear system of the so-called normal equations:

28. 2.2. MRA_based Color Transform(5/20) Multiple Regression Analysis (5/5) • The matrix form solution bewhere

29. 2.2. MRA_based Color Transform(6/20) Figure 13. Target object .

30. 2.2. MRA_based Color Transform(7/20) Figure 14. Source object .

31. 2.2. MRA_based Color Transform(8/20) • Best fitting functions Red Green Blue Figure 15. The curves of degree1, 5, and 9 best fitting functions.

32. 2.2. MRA_based Color Transform(9/20) Figure 16. The color transfer results corresponding to the variation in the degree of best fitting polynomials.

33. 2.2. MRA_based Color Transform(10/20) L* a* b* Figure 17. The box-plots of L*, a*, and b* for the target, source, and color transferred objects in Figure 11.

34. 2.2. MRA_based Color Transform(11/20) Table 2. The measurement metrics for the target, source and color transferred objects in Figure 17 (1/2)

35. 2.2. MRA_based Color Transform(12/20) Table 2. The measurement metrics for the target, source and color transferred objects in Figure 17 (2/2)

36. 2.2. MRA_based Color Transform(13/20) • The target RGB color image is a girl in a blue dress (350×350 pixels). • The source color images with different sizes.

37. 2.2. MRA_based Color Transform(14/20) • The extraction procedure lasted between 3 and 25 seconds, and the color transferring procedure lasted about 0.03 seconds.

38. 2.2. MRA_based Color Transform(15/20) Figure 20. Examples of color transferring between objects with the proposed multiple regression analysis algorithm (1/2).

39. 2.2. MRA_based Color Transform(16/20) Figure 21. Examples of color transferring between objects with the proposed multiple regression analysis algorithm (2/2).

40. 2.2. MRA_based Color Transform(17/20) • Performance measures function:

41. 2.2. MRA_based Color Transform(18/20) Table 3. The measurement metrics for the target and source objects in Figures 20,21

42. 2.2. MRA_based Color Transform(19/20) Table 4. The measurement metrics for the color transferred target objects in Figures 20, 21

43. 2.2. MRA_based Color Transform(20/20) Table 5. The absolute difference in measurement metrics of the transferred target-object from the source object in Figures 20 and 21

44. Conclusions • Simple, effective and accurate in color transferring between objects. • Details of target object can be changed by the color complexity of source object. • Time consumption is independent of the number of bins selected and the degree of regression. • Dynamic ranges of colors of objects don’t have any restriction.

45. Thank You Questions and Comments