Separation of Transparent Layers using Focus
International Journal of Computer Vision
On the Motion and Appearance of Specularities in Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multi-valued Images and Their Separation
Proceedings of the 10th International Workshop on Theoretical Foundations of Computer Vision: Multi-Image Analysis
Transparent Surface Modeling from a Pair of Polarization Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Linear Superposition of Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polarization Multiplexing and Demultiplexing for Appearance-Based Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
The separation of reflected and transparent layers from real-world image sequence
Machine Vision and Applications
Separation of reflection and transparency using epipolar plane image analysis
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Image-based rendering for scenes with reflections
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
Image-based rendering in the gradient domain
ACM Transactions on Graphics (TOG)
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When a transparent surface is present between an observer and an object, an image reflected by the surface may be superimposed on the image of the observed object. We present a new approach to recover the scenes (layers) and to classify which is the reflected/transmitted one, based on imaging through a polarizing filter at two orientations. Estimates of the separate layers are obtained by weighted pixel-wise differences of these images, inverting the image formation process. However, the weights depend on the angle of incidence, hence on the inclination of the transparent (invisible) surface. This angle is estimated by seeking the angle-value which (through the weights) leads to decorrelation of the estimated layers. Experimental results, obtained using real photos of actual objects, demonstrate the success of angle estimation and consequent layer separation and labeling. The method is shown to be superior to earlier methods where only raw optical data was used.