Mairéad Grogan

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This paper proposes to perform colour transfer by minimising a divergence (the L2 distance) between two colour distributions. We propose to model each dataset by a compact Gaussian mixture which is designed for the specific purpose of colour transfer between images which have different scene content. A non rigid transformation is estimated by minimising the(More)
Despite the recent advances in 3D reconstruction from images, the state of the art methods fail to accurately reconstruct objects with reflective materials. The underlying reason for this inaccuracy is that the detected image features belong to the reflected scene instead of the reconstructed object and do not lie on the surface of the object. In this(More)
This paper proposes an algorithm for inferring a 3D mesh using the robust cost function proposed by Ruttle et al. [12]. Our contribution is in proposing a new algorithm for inference that is very suitable for parallel architecture. The cost function also provides a goodness of fit for each element of the mesh which is correlated to the distance to the(More)
This paper investigates how several techniques can be used together for colouring frames in grey level sequences. A trained deep neural network is used to colour a grey level image coherently [Iizuka et al., 2016], and this colour image can be recoloured further to change its feel [Grogan et al., 2015]. When considering videos however, artifacts are created(More)
We present a flexible approach to colour transfer inspired by techniques recently proposed for shape registration. Colour distributions of the palette and target images are modelled with Gaussian Mixture Models (GMMs) that are robustly registered to infer a non linear parametric transfer function. We show experimentally that our approach compares well to(More)
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