98 citations · 163 across the 13 of their papers we have counts for
13 papers · 1 filter
Combining geolocation and height estimation of objects from street level imagery
Matej Ulicny, Vladimir A. Krylov, Julie Connelly +1
We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality…
Model-based inexact graph matching on top of CNNs for semantic scene understanding
Jérémy Chopin, Jean-Baptiste Fasquel, Harold Mouchère +2
Deep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing modu…
3D point cloud segmentation using GIS
Chao-Jung Liu, Vladimir Krylov, Rozenn Dahyot
In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The propos…
Sliced Distance for Colour Grading
Hana Alghamdi, Rozenn Dahyot
We propose a new method with distance that maps one -dimensional distribution to another, taking into account available information about correspondences. We sol…
Iterative Nadaraya-Watson Distribution Transfer for Colour Grading
Hana Alghamdi, Rozenn Dahyot
We propose a new method with Nadaraya-Watson that maps one N-dimensional distribution to another taking into account available information about correspondences. We extend the 2D/3…
Patch based Colour Transfer using SIFT Flow
Hana Alghamdi, Rozenn Dahyot
We propose a new colour transfer method with Optimal Transport (OT) to transfer the colour of a sourceimage to match the colour of a target image of the same scene that may exhibit…
Harmonic Convolutional Networks based on Discrete Cosine Transform
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spec…
Entropic Regularisation of Robust Optimal Transport
Rozenn Dahyot, Hana Alghamdi, Mairead Grogan
Grogan et al [11,12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour dis…
Harmonic Networks with Limited Training Samples
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. Howe…
Harmonic Networks: Integrating Spectral Information into CNNs
Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produ…
Shape Registration with Directional Data
Mairéad Grogan, Rozenn Dahyot
We propose several cost functions for registration of shapes encoded with Euclidean and/or non-Euclidean information (unit vectors). Our framework is assessed for estimation of bot…
Automatic Discovery and Geotagging of Objects from Street View Imagery
Vladimir A. Krylov, Eamonn Kenny, Rozenn Dahyot
Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this…
Robust Registration of Gaussian Mixtures for Colour Transfer
Mairéad Grogan, Rozenn Dahyot
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 modelle…