The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A physical approach to color image understanding
International Journal of Computer Vision
Surface Reflection: Physical and Geometrical Perspectives
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Illuminant Direction, Albedo, and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining Reflectance Properties of an Object Using Range and Brightness Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
Reflectance based object recognition
International Journal of Computer Vision
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Reflectance Model for Computer Graphics
ACM Transactions on Graphics (TOG)
Illumination for computer generated pictures
Communications of the ACM
Image-based rendering of diffuse, specular and glossy surfaces from a single image
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Models of light reflection for computer synthesized pictures
SIGGRAPH '77 Proceedings of the 4th annual conference on Computer graphics and interactive techniques
Solving for Colour Constancy using a Constrained Dichromatic Reflection Model
International Journal of Computer Vision
Estimating Reflection Parameters from a Single Color Image
IEEE Computer Graphics and Applications
A Variational Framework for Retinex
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-Layered Reflection Model of Natural Human Skin
CGI '01 Computer Graphics International 2001
Polarization-based Inverse Rendering from a Single View
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Separating Reflection Components of Textured Surfaces Using a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of human faces using a measurement-based skin reflectance model
ACM SIGGRAPH 2006 Papers
The modified Beckmann-Kirchhoff scattering theory for rough surface analysis
Pattern Recognition
Reflectance Modeling for Layered Dielectrics with Rough Surface Boundaries
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
A layered, heterogeneous reflectance model for acquiring and rendering human skin
ACM SIGGRAPH Asia 2008 papers
International Journal of Computer Vision
A Solution of the Dichromatic Model for Multispectral Photometric Invariance
International Journal of Computer Vision
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In this paper, we present a method to recover the parameters governing the reflection of light from a surface making use of a single hyperspectral image. To do this, we view the image radiance as a combination of specular and diffuse reflection components and present a cost functional which can be used for purposes of iterative least squares optimisation. This optimisation process is quite general in nature and can be applied to a number of reflectance models widely used in the computer vision and graphics communities. We elaborate on the use of these models in our optimisation process and provide a variant of the Beckmann-Kirchhoff model which incorporates the Fresnel reflection term. We show results on synthetic images and illustrate how the recovered photometric parameters can be employed for skin recognition in real world imagery, where our estimated albedo yields a classification rate of 95.09+/-4.26% as compared to an alternative, whose classification rate is of 90.94+/-6.12%. We also show quantitative results on the estimation of the index of refraction, where our method delivers an average per-pixel angular error of 0.15^o. This is a considerable improvement with respect to an alternative, which yields an error of 9.9^o.