arXiv subjects

Rozenn Dahyot

Publications and source records attributed to Rozenn Dahyot.

22 records · Page 2Linked to original sources

arXiv2018

Harmonic Networks: Integrating Spectral Information into CNNs

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 produce features by learning optimal combinations of spectral filters defined by the Discrete Cosine Transform. The harmonic blocks are used to replace conventional convolutional layers to construct partial or fully harmonic CNNs. We extensively validate our approach and show that the introduction of harmonic blocks into state-of-the-art CNN baseline architectures results in comparable or better performance in classification tasks on small NORB, CIFAR10 and CIFAR100 datasets.

cs.CV↗

arXiv2017

Automatic Discovery and Geotagging of Objects from Street View Imagery

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 paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model to perform objects triangulation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with multiple visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and GPS accuracy within 2 meters, which is comparable with the precision of single-frequency GPS receivers.

cs.CV↗

arXiv2017

Shape Registration with Directional Data

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 both rigid and non-rigid transformations between the target and model shapes corresponding to 2D contours and 3D surfaces. The experimental results obtained confirm that using the combination of a point's position and unit normal vector in a cost function can enhance the registration results compared to state of the art methods.

cs.CV↗

arXiv2017

Robust Registration of Gaussian Mixtures for Colour Transfer

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 current techniques both quantitatively and qualitatively. Moreover, our technique is computationally the fastest and can take efficient advantage of parallel processing architectures for recolouring images and videos. Our transfer function is parametric and hence can be stored in memory for later usage and also combined with other computed transfer functions to create interesting visual effects. Overall this paper provides a fast user friendly approach to recolouring of image and video materials.

cs.CV↗