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Object recognition under varying illumination

Learn how objects appear differently under varying illumination using Lambertian and Specular reflections. Discover methods for recognising objects and detecting multiple light sources effectively.

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Object recognition under varying illumination

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  1. Object recognition under varying illumination

  2. Lighting changes objects appearance.

  3. Lambertian Specular 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. albedo surface normal (light intensity)* (light direction) intensity 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 tocos θ.

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

  8. 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. N L R V l r 

  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 Low dimensional representation of an object’s image set under different lightings image rendering compare compare

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

  12. Use specularities for recognition

  13. Matching Specularities hypothesized pose 3D model approximate

  14. Mapping image Gaussian sphere

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

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

  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 Same object render Lambertian component highlight Lambertian component highlight

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

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

  25. Real light source If two regions and are adjacent on the image, with and the corresponding virtual lights then s2 s1 s3

  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.

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