Search Results for author: Graham Finlayson

Found 6 papers, 3 papers with code

Unifying Optimization Methods for Color Filter Design

no code implementations24 Jun 2020 Graham Finlayson, Yuteng Zhu

In this paper we begin by observing that the function defining the Vora-Value is equivalent to the Luther-condition optimization if we use the orthonormal basis of the XYZ color matching functions, i. e. we linearly transform the XYZ sensitivities to a set of orthonormal basis.

NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

1 code implementation7 May 2020 Boaz Arad, Radu Timofte, Ohad Ben-Shahar, Yi-Tun Lin, Graham Finlayson, Shai Givati, others

This paper reviews the second challenge on spectral reconstruction from RGB images, i. e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.

Spectral Reconstruction

Semi-supervised semantic segmentation needs strong, high-dimensional perturbations

no code implementations25 Sep 2019 Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, Graham Finlayson

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems.

Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation needs strong, varied perturbations

3 code implementations5 Jun 2019 Geoff French, Samuli Laine, Timo Aila, Michal Mackiewicz, Graham Finlayson

We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.

General Classification Semi-Supervised Semantic Segmentation

As-projective-as-possible bias correction for illumination estimation algorithms

1 code implementation JOSA A 2019 Mahmoud Afifi, Abhijith Punnappurath, Graham Finlayson, Michael S. Brown

Recent work by Finlayson, Interface Focus, 2018 showed that a bias correction function can be formulated as a projective transform because the magnitude of the R, G, B illumination vector does not matter to the AWB procedure.

Interactive Illumination Invariance

no code implementations20 Jul 2016 Han Gong, Graham Finlayson

We present a user-friendly interactive system for robust illumination-invariant image generation.

Image Generation

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