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A Standardized Workflow for Illumination-Invariant Image Extraction

This paper presents a standardized workflow for achieving illumination-invariant image extraction. The proposed method uses sharpening in sRGB space, which proves to be more effective than other approaches. The results show that the standardized workflow can be a preprocessing step for various vision problems.

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A Standardized Workflow for Illumination-Invariant Image Extraction

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  1. A Standardized Workflow for Illumination-Invariant Image Extraction Mark S. Drew Muntaseer Salahuddin Alireza Fathi Simon Fraser University, Vancouver, Canada {mark,msalahud,alirezaf}@cs.sfu.ca www.cs.sfu.ca/~mark

  2. Introduction • Illumination-invariant image extraction is an interesting and open problem in vision. • illustration shows the objective: (the “intrinsic image”)

  3. Introduction (cont.) • To obtain (b) from (a), we take the logarithm of band-ratio chromaticity colour coordinates, and then project in a special direction[Finlayson and Hordley, 2001]. • The resultant grey-scale image is illumination invariant.

  4. Introduction (cont.) • Objective: we argue that sharpening sRGB allows us to find the invariant image as a generic workflow for images, from • unknown cameras • unknown actual special direction • no complex algorithm using evidence in each image • Works well (but not as well as knowing the camera or using internal evidence in image!)

  5. Shadow Removal Illumination invariant is crucial step!

  6. Finding direction • The direction of projection is crucial.

  7. Finding direction… • Could calibrate the camera to find the invariant direction[Finlayson et al. (2002)]: HP912 Digital Still Camera: Log-chromaticities of 24 patches; 6 patches, imaged under 9 illuminants.

  8. Wrong direction – higher entropy Correct direction – smaller entropy Uses internal evidence in image. Finding direction… • Without calibrating the camera, can use entropy of projection to find the invariant direction [Finlayson et al. (2004)]:

  9. This paper: Sharpening Helps • Argument at AIC05 [Finlayson et al. 2005] : recommended that if we sharpen the values in XYZ space, get better invariant. • HOWEVER…

  10. Proposed Approach: Sharpen sRGB • However, going from sRGB to XYZ is a broadening transform: a counter-intuitive approach. • Therefore we propose to sharpen the sRGB space itself. • Works better!

  11. Old: Sharpen XYZ;new: Sharpen sRGB • Old: Assume input is in nonlinear sRGB space; linearize; transform to XYZ; sharpen XYZ; chromaticity; project  lighting-change direction. • New: Assume input is in nonlinear sRGB space; linearize; sharpen sRGBlinear using synthetic data, producing standardized transform for all images; chromaticity; project  lighting-change direction.

  12. sRGB to XYZ is a Broadening Transform

  13. Comparing XYZ to sRGB: no sharpening XYZ sRGB Log-chromaticity coordinates for Macbeth patches, as light changes.

  14. Comparing sharpening XYZ to sharpening RGB Synthetic: Macbeth + Planckians XYZ R = 0.764 (with Mean Subtraction) sRGB R = 0.920 √ After Sharpening

  15. Standard colour transform • Colour transform is “data-based” sharpening, optimizing lighting-invariance of output (with positivity enforced) [Drew et al. 2002] • The transformation matrix T that does so is the following,

  16. Standard colour transform… • Sharpen, form chromaticities, then project in standard direction

  17. The Standardized Workflow

  18. Apply Standardized method to measured chart data 105 illuminants, Nikon D70 best fit - - - - standardized sRGB sharpening ───

  19. Apply Standardized method: invariant Macbeth chart, under 14 different daylights. HP912 camera.

  20. Best possible: Invariant image, formed by calibrating camera. Av. of Std. Dev. across illuminants= 4.42%

  21. Standardized method: Invariant image, formed by sharpening sRGB. Av. of Std. Dev. across illuminants= 6.11% not as good as calibrated version, of course! but usable.

  22. Standardized method: shadow gone√ input chromaticity, segmented for display output, segmented

  23. Conclusion • The sharpening transform does a good enough job finding an invariant, given that it does not depend on any information specific to the camera or even the image. • It can serve as a preprocessing step to many different vision problems.

  24. Thanks! To Natural Sciences and Engineering Research Council of Canada

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