Surface Dependent Representations for Illumination Insensitive Image Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
A perceptually validated model for surface depth hallucination
ACM SIGGRAPH 2008 papers
Color Subspaces as Photometric Invariants
International Journal of Computer Vision
A Solution of the Dichromatic Model for Multispectral Photometric Invariance
International Journal of Computer Vision
Detecting ground shadows in outdoor consumer photographs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
A theory of spherical harmonic identities for BRDF/Lighting transfer and image consistency
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Estimating the Natural Illumination Conditions from a Single Outdoor Image
International Journal of Computer Vision
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We derive a new class of photometric invariants that can beused for a variety of vision tasks including lighting invariantmaterial segmentation, change detection and tracking, aswell as material invariant shape recognition. The key ideais the formulation of a scene radiance model for the class of"separable" BRDFs, that can be decomposed into materialrelated terms and object shape and lighting related terms.All the proposed invariants are simple rational functions ofthe appearance parameters (say, material or shape and lighting).The invariants in this class differ from one another in thenumber and type of image measurements they require. Mostof the invariants in this class need changes in illumination orobject position between image acquisitions. The invariantscan handle large changes in lighting which pose problems formost existing vision algorithms. We demonstrate the power ofthese invariants using scenes with complex shapes, materials,textures, shadows and specularities.