Proceedings of a workshop on Image understanding workshop
Polarization methods in computer vision
Polarization methods in computer vision
A reflectance model for computer graphics
SIGGRAPH '81 Proceedings of the 8th annual conference on Computer graphics and interactive techniques
A physical approach to color image understanding
A physical approach to color image understanding
Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polarization Phase-Based Method For Material Classification In Computer Vision
International Journal of Computer Vision
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thermophysical Algebraic Invariants from Infrared Imagery for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Motion and Appearance of Specularities in Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Transparent Surface Modeling from a Pair of Polarization Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating Reflection Components of Textured Surfaces Using a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corneal Imaging System: Environment from Eyes
International Journal of Computer Vision
Polarization Multiplexing and Demultiplexing for Appearance-Based Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a 3D Multispectral Scanner: An Application to Multimedia
IEEE MultiMedia
Accurately estimating reflectance parameters for color and gloss reproduction
Computer Vision and Image Understanding
Object separation by polarimetric and spectral imagery fusion
Computer Vision and Image Understanding
Polarization vision: a new sensory approach to image understanding
Image and Vision Computing
Using light polarization in laser scanning
Image and Vision Computing
Circularly polarized spherical illumination reflectometry
ACM SIGGRAPH Asia 2010 papers
Natural material segmentation and classification using polarisation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Vision Based UAV Attitude Estimation: Progress and Insights
Journal of Intelligent and Robotic Systems
Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives
Image and Vision Computing
Shape and Refractive Index from Single-View Spectro-Polarimetric Images
International Journal of Computer Vision
A new projection space for separation of specular-diffuse reflection components in color images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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A computationally simple yet powerful method for distinguishing metal and dielectric material surfaces from the polarization characteristics of specularly reflected light is introduced. The method is completely passive, requiring only the sensing of transmitted radiance of reflected light through a polarizing filter positioned in multiple orientations in front of a camera sensor. Precise positioning of lighting is not required. An advantage of using a polarization-based method for material classification is its immunity to color variations, which so commonly exist on uniform material samples. A simple polarization-reflectance model, called the Fresnel reflectance model, is developed. The fundamental assumptions are that the diffuse component of reflection is completely unpolarized and that the polarization state of the specular component of reflection is dictated by the Fresnel reflection coefficients. The material classification method presented results axiomatically from the Fresnel reflectance model, by estimating the polarization Fresnel ratio. No assumptions are required about the functional form of the diffuse and specular components of reflection. The method is demonstrated on some common objects consisting of metal and dielectric parts.