Polarization-Based Material Classification from Specular Reflection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polarization methods in computer vision
Polarization methods in computer vision
Improving Depth Image Acquisition Using Polarized Light
International Journal of Computer Vision
Two-dimensional BRDF estimation from polarisation
Computer Vision and Image Understanding
Object separation by polarimetric and spectral imagery fusion
Computer Vision and Image Understanding
Circularly polarized spherical illumination reflectometry
ACM SIGGRAPH Asia 2010 papers
Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
Shape and Refractive Index from Single-View Spectro-Polarimetric Images
International Journal of Computer Vision
| Hi-index | 0.00 |
A robust and accurate polarization phase-based technique formaterial classification is presented. The novelty of this technique isthree-fold in (i) its theoretical development, (ii) application, and, (iii)experimental implementation. The concept of phase of polarization of a lightwave is introduced to computer vision for discrimination between materialsaccording to their intrinsic electrical conductivity, such as distinguishingconducting metals, and poorly conducting dielectrics. Previous work has usedintensity, color and polarization component ratios. This new method isbased on the physical principle that metals retard orthogonal components oflight upon reflection while dielectrics do not. This method has significantcomplementary advantages with respect to existing techniques, iscomputationally efficient, and can be easily implemented with existingimaging technology. Experiments for real circuit board inspection,nonconductive and conductive glass, and, outdoor object recognition havebeen performed to demonstrate its accuracy and potential capabilities.