1. Object recognition under varying illumination

2. Lighting changes objects appearance.

3. Specular Lambertian How do we recognize these objects?

4. Few Definitions: Reflection Reflection - The scattering of light from an object. Two extreme cases: diffuse reflection and specular reflection. Real objects reflect light as a mixture of these two extremes.

5. Few Definitions: Lambertian Reflection Surface reflects equally in all directions. –Examples: chalk, clay, cloth, matte paint Brightness doesn’t depend on viewpoint. Amount of light striking surface proportional to cos θ. intensity albedo surface normal (light intensity)* (light direction)

6. Few Definitions: Specular Reflection Specular surfaces reflect light more strongly in some directions than in others. Appearance of a surface depends on the direction L of the light source, direction of the surface normal N, and direction V of viewing. The vectors L, N and R all lie in one plane

7. Few Definitions: Specular Reflection Perfect mirror: The angle of incidence equals the angle of reflection. rough specular R N L mirror R N L θθ Rough specular : Most specular surfaces reflect energy in a tight distribution (or lobe) centered on the optical reflection direction –Examples: metals,glass

8. N L llll R V rrrr Few Definitions: Phong Model Determine the angle α between the direction V of viewing and the direction R of reflection by an ideal mirror. Assume the intensity of reflected light is proportional to cos(α) The exponent n (“shine”) is determined empirically. Large values of n make the surface behave more like an ideal mirror.

9. Phong’s exponent controls how fast the highlight “falls-off”

10. Lambertian Main Approaches 2D methods based on quasi-invariance to lighting Model- based: 3D to 2D 3D image rendering Low dimensional representation of an object’s image set under different lightings compare

11. Main Approaches Specular 2D Methods: will be distracted by highlights and lack of real edges. 3D Methods: Specular objects cannot be well approximated by low- dimensional linear sub- spaces. Apply Lambertian methods and treat specularities as noise ?

12. Use specularities for recognition

13. Matching Specularities hypothesized pose approximate 3D model

14. Mapping image Gaussian sphere

15. Finding Specularity query map onto the sphere consistent specularity disk map back recovered highlights threshold specular candidates

16. Wrong Match query inconsistent map onto the sphere specularity disk map back recovered highlights threshold specular candidates

17. Combined Method for Recognition of General Objects Integrate knowledge about highlights with the Lambertian component. No prior knowledge of lighting. Recover light direction from Lambertian component. No prior knowledge of how specular and how Lambertian the object is.

18. Comparison render Lambertian component Lambertian component highlight highlight Lambertian component Lambertian component highlight highlight Same object

19. Uncontrolled Lighting First step: allow multiple unknown light sources. –Extend the highlight recovery to work with known multiple light sources. –Detect multiple light source directions from the Lambertian component. –Use both Lambertian and specular parts to more robust detection of light sources.

20. PROJECT 5 Extend the specular recognition algorithm* to multiple light sources. Collect a test set of several rotationally symmetric glass objects: - Take images of these objects filled with opaque liquid for 3D model construction. - Take 3 images of each object with 2 and 3 light sources and different backgrounds. Test the algorithm on these objects. * M. Osadchy, D.W. Jacobs and R. Ramamoorthi, Using specularities for recognition, IEEE International Conference on Computer Vision (ICCV), 2003

21. Multiple Light Source Detection Given an image of known shape, recover the light sources.

22. Sphere Illumination Critical Boundary

23. Multiple Light Sources Set of lights that illuminate pixels in Virtual light associated with region

24. Finding Critical Boundaries smalllarge Threshold f Windows with large f correspond to points on critical boundaries. Apply Hough Transform to fit points to critical boundaries.

25. Real light source If two regions and are adjacent on the image, with and the corresponding virtual lights then s1s1 s3s3 s2s2

26. PROJECTS 6 Implement V2R algorithm* on sphere with 3 light sources (no opposite lights). Extend V2R algorithm to textured spherical objects. Large bonus: extend this algorithm to run on arbitrary convex objects. * Christos-Savvas Bouganis, Mike Brookes. "Multiple Light Source Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 4, pp. 509-514, April, 2004.