IEEE Transactions on Pattern Analysis and Machine Intelligence
A theory of locally low dimensional light transport
ACM SIGGRAPH 2007 papers
Using specularities in comparing 3D models and 2D images
Computer Vision and Image Understanding
Estimation of non-negative ODFs using the eigenvalue distribution of spherical functions
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Rotation invariant features for HARDI
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Recognition of specular objects is particularly difficult because their appearance is much more sensitive to lighting changes than that of Lambertian objects. We consider an approach in which we use a 3D model to deduce the lighting that best matches the model to the image. In this case, an important constraint is that incident lighting should be non-negative everywhere. In this paper, we propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The method follows from a novel extension of Szego驴s eigenvalue distribution theorem to spherical harmonics, and uses semidefinite programming to perform a constrained optimization. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models.