Search Results for author: Pablo Márquez-Neila

Found 7 papers, 1 papers with code

Real-Time Camera Pose Estimation for Sports Fields

no code implementations31 Mar 2020 Leonardo Citraro, Pablo Márquez-Neila, Stefano Savarè, Vivek Jayaram, Charles Dubout, Félix Renaut, Andrés Hasfura, Horesh Ben Shitrit, Pascal Fua

Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.

Pose Estimation

Fused Detection of Retinal Biomarkers in OCT Volumes

no code implementations16 Jul 2019 Thomas Kurmann, Pablo Márquez-Neila, Siqing Yu, Marion Munk, Sebastian Wolf, Raphael Sznitman

In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume.

Simulation of hyperelastic materials in real-time using Deep Learning

no code implementations10 Apr 2019 Andrea Mendizabal, Pablo Márquez-Neila, Stéphane Cotin

In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm.

Cantilever Beam

Self-Binarizing Networks

no code implementations2 Feb 2019 Fayez Lahoud, Radhakrishna Achanta, Pablo Márquez-Neila, Sabine Süsstrunk

To obtain similar binary networks, existing methods rely on the sign activation function.


Imposing Hard Constraints on Deep Networks: Promises and Limitations

no code implementations7 Jun 2017 Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua

Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data.

Uniform Information Segmentation

no code implementations27 Nov 2016 Radhakrishna Achanta, Pablo Márquez-Neila, Pascal Fua, Sabine Süsstrunk

Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details.

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