1. Introduction of the intrinsic image

2. Intrinsic Images The method of Finlayson & Hordley （ 2001 ） Two assumptions 1. the camera ’ s sensors are sufficiently narrow band. 2. the illuminant can be approximated by a black-body radiator.

3. The power spectrum of a black-body radiator The radiance of a black-body radiator temperature T （ Kelvin ）, wavelength, Planck ’ s constant Boltzmann ’ s constant, the speed of light

4. Finlayson & Hordley ： many fluorescent light sources can be approximated by a black-body radiator.

5. Simplification of the equation The temperature T ＜ 10000 K. The visual spectrum ranges ： 400 nm ~ 700 nm.

6. Different intensities of the black-body radiator ： another constant k. The intensity measured by the sensor

7. Assumption 1. the illuminant can be approximated by a black-body radiator. 2. pixel colors are linearly related to the data measured by the sensor.

8. Logarithm The first term ： the power of the illuminant and the scene geometry but independent of the color of the illuminant and the reflectance. The second term ： the wavelength to which the sensor responds and on the reflectance of the object. The last term ： the color of illuminant.

9. Compute the differences Let and.

10. As a 2-D vector. If the temperature T is a parameter, the two equations define a line where is a point on the line and other points on the line are reached by adding with varying amounts.

11. The constants only depend on the wavelength to which the sensor responds. is independent of reflectance.

12. T ： 1000 K ~ 10000 K （ in steps of 20 ％） Several different surfaces （ red －＞ white ） were illuminanted by a black-body radiator of different temperatures. sensors ： The direction of the lines only depends on the type of sensors used in the camera.

13. Gamma correction Pixel colors ： logarithm ： The influence of any gamma correction does not exist for the line in log- chromaticity difference space.

14. Removal of the temperature T Project the data points in a direction orthogonal to the line. The equation is independent of the illuminant.

15. Color circle log-chromaticity difference coordinates

16. If we project the data points along the invariant direction, some information will be lost. Mix of blue and red or cyan and yellow. The direction in which to project the log-chromaticity difference is unique for each camera.

17. Drawback ＆ ： the response of the chosen channel may be very low, leading to noisy results. Which channel should be chosen ？

18. Modification Finlayson ＆ Drew （ 2001 ）： dividing by the geometric mean of the three channels to remove the dependence on the shading information G and the dependence on the intensity k.

20. the vector is orthogonal to the vector, all points are located on the 2-D plane defined by u.

21. Finlayson （ 2004 ） define the following coordinate system for the geometric mean chromaticity space. ： the two basis vectors Find the two vectors

22. The geometric mean 2-D chromaticity space Color circle Geometric mean 2-D chromaticity space

24. Geometric mean chromaticities If we project along a line in geometric mean 2-D chromaticity space, we ’ ll lose some information about the color of the objects. Using the method, we can transform the input image of a calibrated camera to an intrinsic image.

25. Intrinsic image The intrinsic images only depend on the reflectance of the object points. The 2-D coordinates have to be projected onto a line that is orthogonal to the vector. For a given camera, this vector can be found experimentally by imaging a calibration image under several different illuminants.

26. Uncalibrated image Finlayson （ 2004 ）： compute intrinsic images for an uncalibrated camera from a single image The projection has to be done onto a line inside the geometric mean 2-D chromaticity space. Which line is the correct one ？

27. The projection of the data points for two different lines.

28. Entropy Finlayson ： the correct orientation is the orientation where the resulting image has minimum entropy. Let be the set of lines for which we compute the entropy.

29. Entropy For each line, the invariant data points A gray-scale image is formed from the projected data points that are transformed to the range[0, 1]. A histogram is computed for the image.

30. Compute the probability that the gray value occurs in the image. Compute the entropy The correct direction for the line will be the one where the entropy is minimal.

31. The invariant direction is located at an angle of 150.75°, which corresponds to a projection direction of 60.75°.

32. The intrinsic image that is based only on reflectance can be used for object recognition. Can we move from an intrinsic image back to a full color image ？ As we have known, some color information is lost.

33. Color image Drew （ 2003 ）： go back to a color image The projected coordinates don ’ t have to be interpreted as a gray-scale image. The coordinates along the projection line are 2-D.

34. Compute the corresponding 3-D coordinates in geometric mean chromaticity space by multiplying the projected coordinates by.

35. Drew ： adding a little illuminant in order to obtain a color image from the 1-D data points. Compute the and project these points onto the invariant direction. Compute the median values of this data.

36. An illuminanted image is obtained by moving all projected data points by along the direction. 1% of the brightest image pixels ＝ the median of the brightest 1% of the image pixels

38. Exponentiate to compute output colors. Intrinsic RGB image Rescale the intrinsic image to have the original lightness of the input image.

39. Segment the input image Compute edges of the segmented input image and the RGB chromaticity intrinsic image Take the threshold ： shadow edges 1. edge values ＞ a threshold in the segmented image 2. edge values ＜ another threshold in the RGB color space

40. Apply the first derivative to the logarithm of the input image Use a morphological operator to binarize and thicken the shadow edges Replace iteratively unknown derivative values on the boundary of the shadow edges by the median of known ones in the vicinity

41. （ e ）＝（ b ）－（ d ） Marc Ebner

43. Graham D. Finlayson

46. Reference Marc Ebner. Color Constancy John Wiley & Sons, England, 2007. Finlayson, G.D. and Drew, M.S. and Lu, C., "Intrinsic Images by Entropy Minimisation", In 8th European Conference on Computer Vision III, pp. 582-595, 2004. G.D. Finlayson, S.D. Hordley, and M.S. Drew. Removing shadows from images. In ECCV 2002: European Conference on Computer Vision, pages 4:823 – 836, 2002. Lecture Notes in Computer Science Vol. 2353, http://www.cs.sfu.ca/ ∼ mark/ftp/Eccv02/shadowless. pdf.